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Publication Date: 2025
International Journal Of Preventive Medicine (20088213) 16
Background: To provide a detailed understanding and apply a comprehensive strategy, this study examines the association between COVID‑19 vaccination and cardiovascular events. We conducted a Bayesian multivariate meta‑analysis using summary data across multiple outcomes including myocardial infarction, stroke, arrhythmia, and CAD, considering potential dependencies in the data. Markov chain Monte Carlo (MCMC) methods were detected for easy implementation of the Bayesian approach. Also, the sensitivity analysis of the model was done by using different priors. Methods: Fifteen studies were included in the systematic review, with eleven studies comparing the results between the vaccine group and the unvaccinated group. Additionally, six studies were used for further analysis to compare mRNA COVID‑19 Vaccines (Pfizer‑BioNTech and Moderna). Results: Bayesian meta‑analysis revealed a link between vaccines and CAD risk (OR, 1.70; 95% CrI: 1.11–2.57), particularly after BNT162b2 (OR, 1.64; 95% CrI: 1.06–2.55) and second dose (OR, 3.44; 95% CrI: 1.99–5.98). No increased risk of heart attack, arrhythmia, or stroke was observed post‑COVID‑19 vaccination. As the only noteworthy point, a protective effect on stroke (OR, 0.19; 95% CrI: 0.10–0.39) and myocardial infarction (OR, 0.003; 95% CrI: 0.001–0.006) was observed after the third dose of the vaccine. Conclusions: Secondary analysis showed no notable disparity in cardiovascular outcomes between BNT162b2 and mRNA vaccines. The association of COVID‑19 vaccination with the risk of coronary artery disease should be considered in future vaccine technologies for the next pandemic. © 2025 International Journal of Preventive Medicine.
Publication Date: 2025
PLoS ONE (19326203) 20(6 June)
Background Reducing the amount of time between the onset of symptoms and presentation to a healthcare facility, namely the “pain-to-door” interval, is of utmost importance in patients with myocardial infarction. In the present study, we aimed to shed light on the psychological, medical, and demographic factors that are associated with this vital time, and the details of this association. Methods We used the baseline data of 1685 participants from a 3-year, multi-centric, cohort study. The pain to door time was estimated as the interval between symptoms’ onset and arrival at the hospital. Patients were asked to fill out valid and reliable questionnaires regarding sociodemographic factors, depression, health anxiety, type D personality, sense of coherence, coping strategies, and quality of life. Data was then analyzed to attain the p-value and hazard ratios (HR) of different variables. Results In the multivariate analysis, being male (HR: 0.81, 95% CI: 0.68–0.98) and a history of angina (0.82, 0.69–0.96) were associated with shorter pain-to-door durations. A history of diabetes mellitus also made the cut marginally (p-value: 0.059). On the contrary, health anxiety (1.27, 1.09–1.49), history of depression (1.57, 1.21–2.05), high socioeconomic status (1.25, 1.03–1.51) and sense of coherence (1.34, 1.14–1.57) scores were associated with longer pain-to-door durations. Conclusion Our findings demonstrate that personal, social, and economical characteristics play a pivotal role in determining patients’ pain-to-door time duration. Screening high-risk individuals in terms of the factors that tend to increase the pain-to-door time alongside educating people and healthcare providers on the importance of this interval and its contributing factors must be a priority considering the devastating burden of CVDs. © 2025 Roohafza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publication Date: 2025
Scientific Reports (20452322) 15(1)
Human Immunodeficiency Virus (HIV) remains a critical public health concern, and is a significant global health challenge, particularly in developing countries. Early HIV detection supports targeted interventions, and substantially reduce the HIV burden. In many resource-limited settings, early detection of HIV is hindered by stigma, limited access to testing, and low risk awareness. This study aims to enhance HIV screening in resource-limited settings by employing machine learning models to predict HIV risk using demographic and lifestyle variables. We analyzed data from 39,295 individuals in Shiraz, Iran, identifying key predictors, including drug injection, age, having a spouse with a history of HIV, occupation, and prison record. We trained and validated an Extreme Gradient Boosting (XGBoost) model using stratified five-fold cross-validation on the dataset. The XGBoost model achieved high accuracy (0.89; Confidence Interval (CI) 95% [0.88–0.89]), very-good discriminatory ability (Area Under the ROC Curve (AUC = 0.84 [0.83–0.84], with a fair-to-good agreement (Cohen’s Kappa of 0.51 [0.51–0.52]). Moreover, the performance of the proposed method (PREDICT-HIV) was consistent across test folds. Our findings align with previous studies, emphasizing the importance of socio-demographic and behavioral factors in HIV risk prediction. The model’s robustness suggests its potential for practical implementation, aiding early identification and intervention in high-risk groups. Future research should incorporate additional socioeconomic variables and validate the model in diverse populations to enhance global HIV prevention efforts. The web application, implemented using the Django framework, is freely available online for public access. PREDICT-HIV may support earlier identification and intervention in underserved populations, improving the efficiency of HIV screening programs. © The Author(s) 2025.
Publication Date: 2025
Hipertension y Riesgo Vascular (18891837) 42(2)pp. 85-93
Introduction: A significant proportion of acute myocardial infarction (MI) patients also suffer from hypertension (HTN), underscoring the need for effective HTN prevention and management strategies in this group. This study aims to elucidate the complex web of direct and indirect factors contributing to HTN in the context of MI. Material and methods: The study utilized longitudinal data from patients aged 18–75 experiencing their first ST-segment elevation MI from five major provinces of Iran, including Tehran, Isfahan, Yazd, Gilan, and Hormozgan. HTN was the primary endpoint, with contributing factors including lifestyle, psychological factors, socioeconomic status, and comorbidities. We applied Bayesian structural equation modeling to analyze the interplay among 14 key variables influencing HTN in MI patients. Results: Among the 1699 participants, 424 men (69.9%) and 181 women (30.1%) were identified as having HTN. Our multi-dimensional analysis revealed that increased comorbidities directly escalate blood pressure levels. Furthermore, the adoption of a healthier lifestyle characterized by sufficient physical activity, quality sleep, sexual satisfaction, non-smoking status, and a favorable dietary score, along with the enhancement of psychosocial factors such as stress management and the modification of type D personality traits and socioeconomic status can curb HTN directly and indirectly. Conclusion: This study integrates diverse factors into a multi-dimensional model and offers insights into new preventive avenues for HTN in MI patients. Our findings can inform strategies to mitigate HTN risk in this vulnerable population by pinpointing both direct and indirect predictors and intervention points. © 2024 SEH-LELHA
Publication Date: 2025
Facets (23711671) 10pp. 1-8
The objective of the present systematic review was to incorporate previous studies investigating the association of birth order with the risk of systolic and diastolic blood pressure (DBP). We employed random-effects and Bayesian meta-analyses, comple-mented by subgroup and sensitivity analyses, including funnel plots, Begg’s rank correlation test, Egger’s linear regression test, Galbraith plots, and leave-one-out meta-analysis. Of the 13 articles analyzed, 92% (12 articles) were published from 2010 onwards. The aggregate sample comprised 466 853 firstborns and 646 786 later-born individuals. Geographically, the studies were primarily conducted in Europe (54%), followed by Asia (23%), and America (23%). The pooled mean difference for systolic blood pressure (SBP) under a random-effects model was 0.28 mm Hg (95% CI: −7.03 to 7.59), and for DBP was 0.33 mm Hg (95% CI: −5.38 to 6.04), neither of which reached statistical significance (SBP: Z = 0.08, P = 0.939; DBP: Z = 0.11, P = 0.910). Sensitivity analyses supported these findings. Bayesian meta-analysis presented a 95% credible interval for SBP and DBP ranging from −7.25 to 7.84 and −5.60 to 6.27, respectively. The investigation found no substantial evidence of a significant difference in SBP and DBP between firstborns and later-born individuals, challenging the hypothesis that birth order significantly impacts blood pressure levels. © 2025 The Author(s).
Publication Date: 2024
Diagnostics (20754418) 14(20)
Background: Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. Methods: We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase–amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject’s label prediction. Results: Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while state-of-the-art features lagged with only 35.71% sensitivity and 28.57% specificity, and db4 with 78.57% sensitivity and 85.71 specificity. PAC significantly outperformed the state-of-the-art features (adj. p-value < 0.001) with a large effect size. However, no significant difference was found between PAC and db4 (adj. p-value = 0.147). Also, the Jeffries–Matusita (JM) distance of the PAC was significantly higher than other features (adj. p-value < 0.001), with a large effect size, suggesting PAC features as robust predictors of neuromuscular diseases, offering a profound understanding of disease pathology and new avenues for interpretation. We evaluated the generalization ability of the PAC model using 99.9% confidence intervals and Bayesian credible intervals to quantify prediction uncertainty across subjects. Both methods demonstrated high reliability, with an expected accuracy of 89% in larger, more diverse populations. Conclusions: This study’s implications might extend beyond LE, paving the way for enhanced diagnostic tools and deeper insights into the complexities of neuromuscular disorders. © 2024 by the authors.
Publication Date: 2024
Cardiovascular Drugs and Therapy (9203206) 39pp. 887-901
Purpose: Radiofrequency (RF) ablation is a prevalent treatment for atrial fibrillation (AF), targeting triggers within the pulmonary vein (PV) for elimination. This study evaluated heart rate variability (HRV) parameter changes at three intervals post-RF ablation: short-term (immediately to 1 month), medium-term (1 to 6 months), and long-term (6 months to 1 year). We compared two ablation techniques: circumferential PV isolation (CPVI) and segmental PV isolation (SPVI). Methods: A thorough search of databases, including PubMed, Embase, Scopus, Web of Science, and Cochrane, in 2022 yielded 835 pertinent studies. After inclusion criteria were applied, 22 studies were analyzed. Results: Results showed a marked decline in HRV parameters post-AF ablation, with LF/HF as an exception. These reductions persisted in short- and long-term evaluations up to a year post-procedure. Subgroup analysis revealed significant HRV declines, with distinct LF/HF values post-SPVI. Conclusion: This meta-analysis suggests the potential of decreased HRV as an indicator of autonomic denervation, necessitating further exploration to optimize therapeutic strategies and enhance patient outcomes. © 2024, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2024
Reviews on Environmental Health (00487554) 39(3)pp. 603-616
Objectives: The effects of man-made electromagnetic fields (EMFs) on the cardiovascular system have been investigated in many studies. In this regard, the cardiac autonomic nervous system (ANS) activity due to EMFs exposure, assessed by heart rate variability (HRV), was targeted in some studies. The studies investigating the relationship between EMFs and HRV have yielded conflicting results. We performed a systematic review and meta-analysis to assess the data's consistency and identify the association between EMFs and HRV measures. Content: Published literature from four electronic databases, including Web of Science, PubMed, Scopus, Embase, and Cochrane, were retrieved and screened. Initially, 1601 articles were retrieved. After the screening, 15 original studies were eligible to be included in the meta-analysis. The studies evaluated the association between EMFs and SDNN (standard deviation of NN intervals), SDANN (Standard deviation of the average NN intervals for each 5min segment of a 24h HRV recording), and PNN50 (percentage of successive RR intervals that differ by more than 50ms). Summary: There was a decrease in SDNN (ES=-0.227 [-0.389, -0.065], p=0.006), SDANN (ES=-0.526 [-1.001, -0.05], p=0.03) and PNN50 (ES=-0.287 [-0.549, -0.024]). However, there was no significant difference in LF (ES=0.061 (-0.267, 0.39), p=0.714) and HF (ES=-0.134 (0.581, 0.312), p=0.556). In addition, a significant difference was not observed in LF/HF (ES=0.079 (-0.191, 0.348), p=0.566). Outlook: Our meta-analysis suggests that exposure to the environmental artificial EMFs could significantly correlate with SDNN, SDANN, and PNN50 indices. Therefore, lifestyle modification is essential in using the devices that emit EMs, such as cell phones, to decrease some signs and symptoms due to EMFs' effect on HRV. © 2023 Walter de Gruyter GmbH, Berlin/Boston.
Publication Date: 2024
Information (Switzerland) (20782489) 15(9)
Cardiometabolic syndrome (CMS) is a growing concern in children and adolescents, marked by obesity, hypertension, insulin resistance, and dyslipidemia. This study aimed to predict CMS using machine learning based on data from the CASPIAN-V study, which involved 14,226 participants aged 7–18 years, with a CMS prevalence of 82.9%. We applied the XGBoost algorithm to analyze key noninvasive variables, including self-rated health, sunlight exposure, screen time, consanguinity, healthy and unhealthy dietary habits, discretionary salt and sugar consumption, birthweight, and birth order, father and mother education, oral hygiene behavior, and family history of dyslipidemia, obesity, hypertension, and diabetes using five-fold cross-validation. The model achieved high sensitivity (94.7% ± 4.8) and specificity (78.8% ± 13.7), with an area under the ROC curve (AUC) of 0.867 ± 0.087, indicating strong predictive performance and significantly outperformed triponderal mass index (TMI) (adjusted paired t-test; p < 0.05). The most critical selected modifiable factors were sunlight exposure, screen time, consanguinity, healthy and unhealthy diet, dietary fat type, and discretionary salt consumption. This study emphasizes the clinical importance of early identification of at-risk individuals to implement timely interventions. It offers a promising tool for CMS risk screening. These findings support using predictive analytics in clinical settings to address the rising CMS epidemic in children and adolescents. © 2024 by the authors.
Publication Date: 2024
Life (20751729) 14(9)
This study aims to develop and apply multistate models to estimate, forecast, and manage hospital length of stay during the COVID-19 epidemic without using any external packages. Data from Bellvitge University Hospital in Barcelona, Spain, were analyzed, involving 2285 hospitalized COVID-19 patients with moderate to severe conditions. The implemented multistate model includes transition probabilities and risk rates calculated from transitions between defined states, such as admission, ICU transfer, discharge, and death. In addition to examining key factors like age and gender, diabetes, lymphocyte count, comorbidity burden, symptom duration, and different COVID-19 waves were analyzed. Based on the model, patients hospitalized stay an average of 11.90 days before discharge, 2.84 days before moving to the ICU, or 34.21 days before death. ICU patients remain for about 24.08 days, with subsequent stays of 124.30 days before discharge and 35.44 days before death. These results highlight hospital stays’ varying durations and trajectories, providing critical insights into patient flow and healthcare resource utilization. Additionally, it can predict ICU peak loads for specific subgroups, aiding in preparedness. Future work will integrate the developed code into the hospital’s Health Information System (HIS) following ISO 13606 EHR standards and implement recursive methods to enhance the model’s efficiency and accuracy. © 2024 by the authors.
Publication Date: 2023
Current Problems in Cardiology (1462806) (7)
This study aims to provide a comprehensive risk-assessment model including lifestyle, psychological parameters, and traditional risk factors to determine the risk of major adverse cardiovascular events (MACE) in patients with the first acute ST-segment elevation myocardial infarction episode. Patients were recruited from new hospital admissions of acute ST-segment elevation myocardial infarction and will be followed up to 3 years. Clinical and paraclinical characteristics, lifestyle, psychological, and MACE information are collected and will be used in the risk-assessment model. Totally, 1707 patients were recruited (male: 81.4%, mean age: 56.60 ± 10.34). Primary percutaneous coronary intervention was the most prevalent type of coronary revascularization (81.9%). In case of baseline psychological characteristics, mean depression score was 5.40 ± 4.88, and mean distress score was 7.64 ± 5.08. A comprehensive approach, focusing on medical, lifestyle, and psychological factors, will lead to better identification of cardiovascular disease patients at risk of developing MACE through comprehensive risk-assessment models. © 2022 Elsevier Inc.
Publication Date: 2022
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 23-74
Brain-computer interface (BCI) aims to translate human intention into a control output signal. In motor-imaginary (MI) BCI, the imagination of movement modifies the cortex brain activity. Such activities are then used in pattern recognition to identify certain movement classes. MI-BCI could be used to enhance the life quality of physically impaired subjects. Several challenges exist in MI-BCI, including selecting appropriate channels, usually linked with a suitable classifier choice. The entire procedure must be real time in practical applications. A variety of channel selection and classification methods were used for MI-BCI in the literature. Also, hybrid machine learning (ML) and deep learning (DL) methods were used in the literature. In this chapter, different channel selection, ML and DL methods, validation frameworks, and performance indices of EEG-based methods were investigated. Three hundred and twenty-two papers published between January 2000 and March 2021 were analyzed in this systematic review. Specific challenges and future directions were then provided. © 2022 Elsevier Inc. All rights reserved.
Publication Date: 2022
IEEE Journal of Biomedical and Health Informatics (21682194) (2)pp. 515-526
A non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from the maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of the FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal to map the MECG to the FECG. The high correlation between maternal and fetal ECG fragments decreases the performance of convolution layers. Therefore, the masking region of interest based on the attention mechanism was performed to improve the signal generators' precision. The sine activation function was also used to retain more details when converting two signal domains. Three available datasets from the Physionet, including the A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, were used to evaluate the performance. The proposed method could map an abdominal MECG to a scalp FECG with an average of 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on the A&D FECG dataset. Moreover, it achieved 99.7% F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on the A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. Also, the distortion was in the 'very good' and 'good' ranges. These results are comparable to the state-of-the-art results; thus, the proposed algorithm has the potential to be used for high-performance signal-to-signal conversion. © 2013 IEEE.
Publication Date: 2022
Frontiers in Neuroscience (16624548)
The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of density, and the trimmed mean absolute value of derivative density were computed for each sEMG channel. These features were tested for the classification of single tasks as well as of two tasks concurrently performed. For single tasks, correlation-based feature selection was used, and the features were then classified using linear discriminant analysis (LDA), non-linear support vector machines, and multi-layer perceptron. The eXtreme gradient boosting (XGBoost) classifier was used for the classification of two movements simultaneously performed. The second and third versions of the Ninapro dataset (conventional control) and Ameri’s movement dataset (simultaneous control) were used to test the proposed features. For the Ninapro dataset, the overall accuracy of LDA using the TMD feature was 98.99 ± 1.36% and 92.25 ± 9.48% for able-bodied and amputee subjects, respectively. Using ensemble learning of the three classifiers, the average macro and micro-F-score, macro recall, and precision on the validation sets were 98.23 ± 2.02, 98.32 ± 1.93, 98.32 ± 1.93, and 98.88 ± 1.31%, respectively, for the intact subjects. The movement misclassification percentage was 1.75 ± 1.73 and 3.44 ± 2.23 for the intact subjects and amputees. The proposed features were significantly correlated with the movement classes [Generalized Linear Model (GLM); P-value < 0.05]. An accurate online implementation of the proposed algorithm was also presented. For the simultaneous control, the overall accuracy was 99.71 ± 0.08 and 97.85 ± 0.10 for the XGBoost and LDA classifiers, respectively. The proposed features are thus promising for conventional and simultaneous myoelectric control. Copyright © 2022 Ghaderi, Nosouhi, Jordanic, Marateb, Mañanas and Farina.
Publication Date: 2022
Brain-Computer Interfaces (2326263X) (3)pp. 155-168
Various algorithms for recognizing Steady-State Visual Evoked Potentials have been proposed over the last decade for realizing Brain-Computer Interfaces. However, frequency-domain techniques aside from classical FFT have been generally neglected. While close to perfect accuracies have been reported in SSVEP-based BCI studies, achieving high accuracy in a realistic scenario is still challenging. Here several frequency-domain algorithms were evaluated for SSVEP detection for the first time, and a new algorithm based on spectral averaging on resampled signal (SAoRS) was proposed, when a single EEG channel and high-frequency flickers were considered to improve user experience. Spectral Envelope (SE) and Maximum Entropy (ME) methods outperformed Burg, MUSIC, and Welch for processing window lengths of 0.5–2 s. The newly developed SAoRS algorithm significantly outperformed SE and the benchmark CCA algorithms for 0.5 s processing window. The results suggest that Spectral Envelop and SAoRS algorithms can provide high accuracies in SSVEP BCI systems. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2021
Diagnostics (20754418) (3)
The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer’s disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Publication Date: 2021
Bmc Medical Research Methodology (14712288) (1)
Background: Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. Methods: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. Results: Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835–0.910]). Conclusions: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions. © 2021, The Author(s).
Publication Date: 2021
Frontiers in Medicine (2296858X)
Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94–98], specificity of 95% [90–99], positive predictive value (PPV) of 99% [98–100], negative predictive value (NPV) of 82% [76–89], diagnostic odds ratio (DOR) of 496 [198–1,245], area under the ROC (AUC) of 0.96 [0.94–0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85–0.88], accuracy of 96% [94–98], and Cohen's Kappa of 0.86 [0.81–0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96–0.98] and 0.92 [0.91–0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate. Copyright © 2021 Marateb, Ziaie Nezhad, Mohebian, Sami, Haghjooy Javanmard, Dehghan Niri, Akafzadeh-Savari, Mansourian, Mañanas, Wolkewitz and Binder.
Publication Date: 2021
Global Heart (22118160) (1)
Background: Developing simplified risk assessment model based on non-laboratory risk factors that could determine cardiovascular risk as accurately as laboratory-based one can be valuable, particularly in developing countries where there are limited resources. Objective: To develop a simplified non-laboratory cardiovascular disease risk assessment chart based on previously reported laboratory-based chart and evaluate internal and external validation, and recalibration of both risk models to assess the performance of risk scoring tools in other population. Methods: A 10-year non-laboratory-based risk prediction chart was developed for fatal and non-fatal CVD using Cox Proportional Hazard regression. Data from the Isfahan Cohort Study (ICS), a population-based study among 6504 adults aged ≥ 35 years, followed-up for at least ten years was used for the non-laboratory-based model derivation. Participants were followed up until the occurrence of CVD events. Tehran Lipid and Glucose Study (TLGS) data was used to evaluate the external validity of both non-laboratory and laboratory risk assessment models in other populations rather than one used in the model derivation. Results: The discrimination and calibration analysis of the non-laboratory model showed the following values of Harrell's C: 0.73 (95% CI 0.71-0.74), and Nam-D'Agostino X2:11.01 (p = 0.27), respectively. The non-laboratory model was in agreement and classified high risk and low risk patients as accurately as the laboratory one. Both non-laboratory and laboratory risk prediction models showed good discrimination in the external validation, with Harrell's C of 0.77 (95% CI 0.75-0.78) and 0.78 (95% CI 0.76-0.79), respectively. Conclusions: Our simplified risk assessment model based on non-laboratory risk factors could determine cardiovascular risk as accurately as laboratory-based one. This approach can provide simple risk assessment tool where laboratory testing is unavailable, inconvenient, and costly. © 2021 Elsevier B.V.. All rights reserved.
Publication Date: 2021
Journal of Electromyography and Kinesiology (10506411)
It is necessary to decompose the intra-muscular EMG signal to extract motor unit action potential (MUAP) waveforms and firing times. Some algorithms were proposed in the literature to resolve superimposed MUAPs, including Peel-Off (PO), branch and bound (BB), genetic algorithm (GA), and particle swarm optimization (PSO). This study aimed to compare these algorithms in terms of overall accuracy and running time. Two sets of two-to-five MUAP templates (set1: a wide range of energies, and set2: a high degree of similarity) were used. Such templates were time-shifted, and white Gaussian noise was added. A total of 1000 superpositions were simulated for each template and were resolved using PO (also, POI: interpolated PO), BB, GA, and PSO algorithms. The generalized estimating equation was used to identify which method significantly outperformed, while the overall rank product was used for overall ranking. The rankings were PSO, BB, GA, PO, and POI in the first, and BB, PSO, GA, PO, POI in the second set. The overall ranking was BB, PSO, GA, PO, and POI in the entire dataset. Although the BB algorithm is generally fast, there are cases where the BB algorithm is too slow and it is thus not suitable for real-time applications. © 2020 Elsevier Ltd
Publication Date: 2020
Public Health Nutrition (13689800) (2)pp. 202-213
Objective: To assess agreement between established methods of estimating salt intake from spot urine collections and 24 h urinary Na (24hUNa) and then to develop a valid formula that can be used in the Iranian population to estimate salt intake from spot urine samples.Design: A validation study. Three spot urine samples were collected (fasting second-void morning; afternoon; evening) on the same day as a 24 h urine collection. We estimated 24hUNa from spot specimens using the Kawasaki, Tanaka and INTERSALT equations. Two new formulas were developed, the Iran formula 1 (Iran 1) and Iran formula 2 (Iran 2), based on our population characteristics.Setting: Iranian adults recruited in 2014-2015.Participants: Healthy volunteer adults aged ≥18 years.Results: With all three spot urine specimens, predicted population 24hUNa was underestimated based on the INTERSALT equation (-469 to -708 mg/d; all P < 0·05) and conversely overestimation occurred with the Kawasaki equation (926 to 1080 mg/d; all P < 0·01). The Tanaka equation produced comparable estimates to measured 24hUNa (-151 to 86 mg/d; all P > 0·49). The newly derived formulas, Iran 1 and Iran 2, showed less mean bias than the established equations (Iran 1: 43 to 80 mg/d, all P > 0·55; Iran 2: 22 to 90 mg/d, all P > 0·50).Conclusions: In this Iranian sample, the Tanaka equation and newly derived formulas produced group-level estimates comparable to measured 24hUNa. The newly developed formulas showed less mean bias than established equations; however, they need to be tested for generalization in a larger sample. © 2019 The Authors.
Publication Date: 2020
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 4-1
Epilepsy, a significant cause of disability and mortality and one of the most common neurological disorders, is associated with seemingly random incidences of recurrent seizures. It affects one percent of the world’s population and individuals of all ages and has a worldwide distribution. About two-thirds of such patients could benefit from antiepileptic drugs. Resective surgery is a possible treatment for the other group (i.e. refractory epilepsy). In focal epilepsy, where a limited area in the brain is involved in abnormal neural activity, the identification of the epileptogenic zone (i.e. the seizure onset zone) is critical. In addition, preventing seizures using medication is facilitated by the prediction of epileptic seizures. In this chapter, we discuss how intracranial or scalp electroencephalographic and magnetoencephalographic recordings could be used for epileptic seizure prediction and onset zone localization. The signal processing methods and the challenges of the state-of-the-art are discussed. © IOP Publishing Ltd 2020.
Publication Date: 2020
Scientific Data (20524463) (1)
This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as “bad” channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses. © 2020, The Author(s).
Publication Date: 2020
IEEE Transactions on Neural Systems and Rehabilitation Engineering (15344320) (12)pp. 2762-2772
Brain-computer interfaces based on code-modulated visual evoked potentials provide high information transfer rates, which make them promising alternative communication tools. Circular shifts of a binary sequence are used as the flickering pattern of several visual stimuli, where the minimum correlation between them is critical for recognizing the target by analyzing the EEG signal. Implemented sequences have been borrowed from communication theory without considering visual system physiology and related ergonomics. Here, an approach is proposed to design optimum stimulus sequences considering physiological factors, and their superior performance was demonstrated for a 6-target c-VEP BCI system. This was achieved by defining a time-factor index on the frequency response of the sequence, while the autocorrelation index ensured a low correlation between circular shifts. A modified version of the non-dominated sorting genetic algorithm II (NSGAII) multi-objective optimization technique was implemented to find, for the first time, 63-bit sequences with simultaneously optimized autocorrelation and time-factor indexes. The selected optimum sequences for general (TFO) and 6-target (6TO) BCI systems, were then compared with m-sequence by conducting experiments on 16 participants. Friedman tests showed a significant difference in perceived eye irritation between TFO and m-sequence (p = 0.024). Generalized estimating equations (GEE) statistical test showed significantly higher accuracy for 6TO compared to m-sequence (p = 0.006). Evaluation of EEG responses showed enhanced SNR for the new sequences compared to m-sequence, confirming the proposed approach for optimizing the stimulus sequence. Incorporating physiological factors to select sequence(s) used for c-VEP BCI systems improves their performance and applicability. © 2001-2011 IEEE.
Publication Date: 2020
Lipids in Health and Disease (1476511X) (1)
Background: A comprehensive study on the interaction of cardiovascular disease (CVD) risk factors is critical to prevent cardiovascular events. The main focus of this study is thus to understand direct and indirect relationships between different CVD risk factors. Methods: A longitudinal data on adults aged ≥35 years, who were free of CVD at baseline, were used in this study. The endpoints were CVD events, whereas their measurements were demographic, lifestyle components, socio-economics, anthropometric measures, laboratory findings, quality of life status, and psychological factors. A Bayesian structural equation modelling was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, coronary syndrome (ACS), and fatal CVDs. Results: In this study, a total of 3161 individuals with complete information were involved in the study. A total of 407 CVD events, with an average age of 54.77(10.66) years, occurred during follow-up. The causal associations between six latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile, with the coefficient of 0.26 (0.01), influenced the occurrence of CVD events as the most critical factor, while it was indirectly mediated through risky behaviours and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy lifestyle components. Conclusions: Analysing a causal network of risk factors revealed the flow of information in direct and indirect paths. It also determined predictors and demonstrated the utility of integrating multi-factor data in a complex framework to identify novel preventable pathways to reduce the risk of CVDs. © 2020 The Author(s).
Publication Date: 2020
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 7-1
The electrical activity generated in contracting muscles is measured using electromyographic (EMG) signals. By placing an array of electrodes on the surface of the skin, surface EMG (sEMG) signals are recorded non-invasively. The sEMG signal is a stochastic signal whose amplitude is generally between 1 and 10 mV, with the most dominant spectral power between 50 and 150 Hz. The sEMG signal is often corrupted by various types of noise, such as movement artifacts, power-line interference and activity from the other muscles. Moreover, the electrode placement could affect the recorded signals. They make analysing and classifying sEMG signals difficult. sEMG has applications in rehabilitation, sport science, kinesiology, ergonomics, muscle architecture identification, neurological disease diagnosis, prosthesis control and human-machine interfaces. In this chapter, the sEMG signal processing methods used in such applications are discussed, and critical issues are considered. Finally, a basic prosthesis control example is provided for interested readers. © IOP Publishing Ltd 2020.
Publication Date: 2019
Basic And Clinical Neuroscience (2008126X) (3)pp. 245-256
Introduction: Brain-Computer Interface (BCI) systems provide a communication pathway between users and systems. BCI systems based on Steady-State Visually Evoked Potentials (SSVEP) are widely used in recent decades. Different feature extraction methods have been introduced in the literature to estimate SSVEP responses to BCI applications. Methods: In this study, the new algorithms, including Canonical Correlation Analysis (CCA), Least Absolute Shrinkage and Selection Operator (LASSO), L1-regularized Multi-way CCA (L1-MCCA), Multi-set CCA (MsetCCA), Common Feature Analysis (CFA), and Multiple Logistic Regression (MLR) are compared using proper statistical methods to determine which one has better performance with the least number of EEG electrodes. Results: It was found that MLR, MsetCCA, and CFA algorithms provided the highest performances and significantly outperformed CCA, LASSO, and L1-MCCA algorithms when using 8 EEG channels. However, when using only 1 or 2 EEG channels d, CFA method provided the highest F-scores. This algorithm not only outperformed MLR and MsetCCA when applied on different electrode montages but also provided the fastest computation time on the test set. Conclusion: Although MLR method has already demonstrated to have higher performance in comparison with other frequency recognition algorithms, this study showed that in a practical SSVEP-based BCI system with 1 or 2 EEG channels and short-time windows, CFA method outperforms other algorithms. Therefore, it is proposed that CFA algorithm is a promising choice for the expansion of practical SSVEP-based BCI systems. © 2019 Iran University of Medical Sciences. All rights reserved.
Publication Date: 2019
Frontiers in Computational Neuroscience (16625188)
Despite the progress in understanding of neural codes, the studies of the cortico-muscular coupling still largely rely on interferential electromyographic (EMG) signal or its rectification for the assessment of motor neuron pool behavior. This assessment is non-trivial and should be used with precaution. Direct analysis of neural codes by decomposing the EMG, also known as neural decoding, is an alternative to EMG amplitude estimation. In this study, we propose a fully-deterministic hybrid surface EMG (sEMG) decomposition approach that combines the advantages of both template-based and Blind Source Separation (BSS) decomposition approaches, a.k.a. guided source separation (GSS), to identify motor unit (MU) firing patterns. We use the single-pass density-based clustering algorithm to identify possible cluster representatives in different sEMG channels. These cluster representatives are then used as initial points of modified gradient Convolution Kernel Compensation (gCKC) algorithm. Afterwards, we use the Kalman filter to reduce the noise impact and increase convergence rate of MU filter identification by gCKC. Moreover, we designed an adaptive soft-thresholding method to identify MU firing times out of estimated MU spike trains. We tested the proposed algorithm on a set of synthetic sEMG signals with known MU firing patterns. A grid of 9 × 10 monopolar surface electrodes with 5-mm inter-electrode distances in both directions was simulated. Muscle excitation was set to 10, 30, and 50%. Colored Gaussian zero-mean noise with the signal-to-noise ratio (SNR) of 10, 20, and 30 dB, respectively, was added to 16 s long sEMG signals that were sampled at 4,096 Hz. Overall, 45 simulated signals were analyzed. Our decomposition approach was compared with gCKC algorithm. Overall, in our algorithm, the average numbers of identified MUs and Rate-of-Agreement (RoA) were 16.41 ± 4.18 MUs and 84.00 ± 0.06%, respectively, whereas the gCKC identified 12.10 ± 2.32 MUs with the average RoA of 90.78 ± 0.08%. Therefore, the proposed GSS method identified more MUs than the gCKC, with comparable performance. Its performance was dependent on the signal quality but not the signal complexity at different force levels. The proposed algorithm is a promising new offline tool in clinical neurophysiology. © 2019 Mohebian, Marateb, Karimimehr, Mañanas, Kranjec and Holobar.
Publication Date: 2018
Frontiers in Neuroscience (16624548) (NOV)
A lot of efforts have been made to understand the structure and function of neocortical circuits. In fact, a promising way to understand the functions of cortical circuits is the classification of the neural types, based on their different properties. Recent studies focused on applying modern computational methods to classify neurons based on molecular, morphological, physiological, or mixed of these criteria. Although there are studies in the literature on in vitro/vivo extracellular or in vitro intracellular recordings, a study on the classification of neuronal types using in vivo whole-cell patch-clamp recordings is still lacking. We thus proposed a novel semi-supervised classification method based on waveform shape of neurons' spikes using in vivo whole-cell patch-clamp recordings. We, first, detected spike candidates. Then discriminative features were extracted from the time samples of the spikes using discrete cosine transform. We then extracted the center of clusters using fuzzy c-mean clustering and finally, the neurons were classified using the minimum distance classifier. We distinguished three types of neurons: excitatory pyramidal cells (Pyr) and two types of inhibitory neurons: GABAergic-parvalbumin positive (PV), and somatostatin positive (SST) non-pyramidal cells in layer II/III of the mice primary visual cortex. We used 10-fold cross validation in our study. The classification accuracy for PV, Pyr, and SST was 91.59 ± 1.69, 97.47 ± 0.67, and 89.06 ± 1.99, respectively. Overall, the algorithm correctly classified 92.67 ± 0.54% of the cells, confirming the relative robustness of the discriminant functions. The performance of the method was further assessed on in vitro recordings by using a pool of 50 neurons from Allen institute Cell Types Database (5 major subtypes of neurons: Pyr, PV, SST, 5HT3a, and vasoactive intestinal peptide (VIP) cells). Its overall accuracy was 84.13 ± 0.81% on this data set using cross validation framework. The proposed algorithm is thus a promising new tool in recognizing cell's type with high accuracy in laboratories using in vivo/vitro whole-cell patch-clamp recording technique. The developed programs and the entire dataset are available online to interested readers. Copyright © 2018 Ghaderi, Marateb and Safari.
Publication Date: 2018
Journal Of Neuroengineering And Rehabilitation (17430003) (1)
Background: In this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-The-Art (SOA) control scheme using a Fitts' law approach. Methods: Seven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized by six metrics. Results: Both methods allowed a completion rate close to 100%, however AEN outperformed SOA for all other performance metrics, e.g. it allowed to perform the tasks on average in half the time with respect to SOA. Moreover, the amount of information transferred by the proposed method in bit/s was nearly twice the throughput of SOA. Conclusions: These results show that autoencoders can map EMG signals into kinematics with the potential of providing intuitive and dexterous control of artificial limbs for amputees. © 2018 The Author(s).
Publication Date: 2018
Computational and Structural Biotechnology Journal (20010370) pp. 121-130
Dyslipidemia, the disorder of lipoprotein metabolism resulting in high lipid profile, is an important modifiable risk factor for coronary heart diseases. It is associated with more than four million worldwide deaths per year. Half of the children with dyslipidemia have hyperlipidemia during adulthood, and its prediction and screening are thus critical. We designed a new dyslipidemia diagnosis system. The sample size of 725 subjects (age 14.66 ± 2.61 years; 48% male; dyslipidemia prevalence of 42%) was selected by multistage random cluster sampling in Iran. Single nucleotide polymorphisms (rs1801177, rs708272, rs320, rs328, rs2066718, rs2230808, rs5880, rs5128, rs2893157, rs662799, and Apolipoprotein-E2/E3/E4), and anthropometric, life-style attributes, and family history of diseases were analyzed. A framework for classifying mixed-type data in imbalanced datasets was proposed. It included internal feature mapping and selection, re-sampling, optimized group method of data handling using convex and stochastic optimizations, a new cost function for imbalanced data and an internal validation. Its performance was assessed using hold-out and 4-foldcross-validation. Four other classifiers namely as supported vector machines, decision tree, and multilayer perceptron neural network and multiple logistic regression were also used. The average sensitivity, specificity, precision and accuracy of the proposed system were 93%, 94%, 94% and 92%, respectively in cross validation. It significantly outperformed the other classifiers and also showed excellent agreement and high correlation with the gold standard. A non-invasive economical version of the algorithm was also implemented suitable for low- and middle-income countries. It is thus a promising new tool for the prediction of dyslipidemia. © 2018 The Authors
Publication Date: 2018
Computer Methods and Programs in Biomedicine (1692607) pp. 103-109
Background and objective: Cochlear implants (CIs) are electronic devices restoring partial hearing to deaf individuals with profound hearing loss. In this paper, a new plug-in for traditional IIR filter-banks (FBs) is presented for cochlear implants based on wavelet neural networks (WNNs). Having provided such a plug-in for commercially available CIs, it is possible not only to use available hardware in the market but also to optimize their performance compared with the-state-of-the-art. Methods: An online database of Dutch diphone perception was used in our study. The weights of the WNNs were tuned using particle swarm optimization (PSO) on a training set (speech-shaped noise (SSN) of 2 dB SNR), while its performance was assessed on a test set in terms of objective and composite measures in the hold-out validation framework. The cost function was defined based on the combination of mean square error (MSE), short‑time objective intelligibility (STOI) criteria on the training set. Variety of performance indices were used including segmental signal- to -noise ratio (SNRseg), MSE, STOI, log-likelihood ratio (LLR), weighted spectral slope (WSS), and composite measures Csig Cbak and Covl. Meanwhile, the following CI speech processing techniques were used for comparison: traditional FBs, dual resonance nonlinear (DRNL) and simple dual path nonlinear (SPDN) models. Results: The average SNRseg, MSE, and LLR values for the WNN in the entire data set were 2.496 ± 2.794, 0.086 ± 0.025 and 2.323 ± 0.281, respectively. The proposed method significantly improved MSE, SNR, SNRseg, LLR, Csig Cbak and Covl compared with the other three methods (repeated-measures analysis of variance (ANOVA); P < 0.05). The average running time of the proposed algorithm (written in Matlab R2013a) on the training and test sets for each consonant or vowel on an Intel dual-core 2.10 GHz CPU with 2GB of RAM was 9.91 ± 0.87 (s) and 0.19 ± 0.01 (s), respectively. Conclusions: The proposed algorithm is accurate and precise and is thus a promising new plug-in for traditional CIs. Although the tuned algorithm is relatively fast, it is necessary to use efficient vectorized implementations for real-time CI speech signal processing. © 2018 Elsevier B.V.
Publication Date: 2017
Computational and Structural Biotechnology Journal (20010370) pp. 75-85
Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence. © 2016 The Authors
Publication Date: 2017
Sensors (14248220) (7)
Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
Publication Date: 2017
International Journal of Neural Systems (1290657) (6)
The neural command from motor neurons to muscles - sometimes referred to as the neural drive to muscle - can be identified by decomposition of electromyographic (EMG) signals. This approach can be used for inferring the voluntary commands in neural interfaces in patients with limb amputations. This paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure. No attempt was made to resolve superimposed motor unit action potentials. The proposed algorithm was validated on sets of simulated and experimental iEMG signals. Signals were recorded from the biceps femoris long-head, vastus medialis and lateralis and tibialis anterior muscles during low-to-moderate isometric constant-force and linearly-varying force contractions. The average number of missed, duplicated and erroneous clusters for the examined signals was 0.5±0.8, 1.2±1.0, and 1.0±0.8, respectively. The average decomposition accuracy (defined similar to signal detection theory but without using True Negatives in the denominator) and coefficient of determination (variance accounted for) for the cumulative discharge rate estimation were 70±9%, and 94±5%, respectively. The time cost for processing each 200ms iEMG interval was 43±16 (21-97)ms. However, computational time generally increases over time as a function of frames/signal epochs. Meanwhile, the incremental accuracy defined as the accuracy of real-time analysis of each signal epoch, was 74±18% for epochs recorded after initial one second. The proposed algorithm is thus a promising new tool for neural decoding in the next-generation of prosthetic control. © 2017 The Author(s).
Publication Date: 2017
Journal Of Research In Medical Sciences (17351995) (9)
Background: In this study, we aimed to determine comprehensive maternal characteristics associated with birth weight using Bayesian modeling. Materials and Methods: A total of 526 participants were included in this prospective study. Nutritional status, supplement consumption during the pregnancy, demographic and socioeconomic characteristics, anthropometric measures, physical activity, and pregnancy outcomes were considered as effective variables on the birth weight. Bayesian approach of complex statistical models using Markov chain Monte Carlo approach was used for modeling the data considering the real distribution of the response variable. Results: There was strong positive correlation between infant birth weight and the maternal intake of Vitamin C, folic acid, Vitamin B3, Vitamin A, selenium, calcium, iron, phosphorus, potassium, magnesium as micronutrients, and fiber and protein as macronutrients based on the 95% high posterior density regions for parameters in the Bayesian model. None of the maternal characteristics had statistical association with birth weight. Conclusion: Higher maternal macro- and micro-nutrient intake during pregnancy was associated with a lower risk of delivering low birth weight infants. These findings support recommendations to expand intake of nutrients during pregnancy to high level. © 2017 Journal of Research in Medical Sciences.
Publication Date: 2017
International Journal of Endocrinology and Metabolism (1726913X) (3)
Background: To investigate the associations of genetic polymorphism with high-density lipoprotein-cholesterol (HDL-C) levels in Iranian adolescents. Methods: This multicentre study was conducted on 10 - 18 year-old students from 27 provinces in Iran. Logic regression approach was used to determine the main effects and interactions of polymorphisms related to HDL-C levels. Results: The rs708272 polymorphism was significantly related to HDL-C levels. Moreover, rs708272 increased HDL-C levels and had a protective effect on HDL-C. The interaction of rs2230808 and rs5880 polymorphisms as well as the interaction of rs320 and rs708272 polymorphisms were associated with lower HDL-C levels. Furthermore, the interaction of rs320 and rs1801177 polymorphisms was associated with lower HDL-C levels. Conclusions:We found that not only single SNPs, but also interactions of several SNPs affect HDL-C levels. Given the high prevalence of low HDL-C in Middle Eastern populations, further genetic studies are required for detailed analysis. © 2017.
Publication Date: 2017
IEEE Transactions on Biomedical Engineering (189294) (7)pp. 1513-1523
Objective: The aim of this study was to reconstruct low-quality High-density surface EMG (HDsEMG) signals, recorded with 2-D electrode arrays, using image inpainting and surface reconstruction methods. Methods: It is common that some fraction of the electrodes may provide low-quality signals. We used variety of image inpainting methods, based on partial differential equations (PDEs), and surface reconstruction methods to reconstruct the time-averaged or instantaneous muscle activity maps of those outlier channels. Two novel reconstruction algorithms were also proposed. HDsEMG signals were recorded from the biceps femoris and brachial biceps muscles during low-to-moderate-level isometric contractions, and some of the channels (5-25%) were randomly marked as outliers. The root-mean-square error (RMSE) between the original and reconstructed maps was then calculated. Results: Overall, the proposed Poisson and wave PDE outperformed the other methods (average RMSE 8.7 μVrms ± 6.1 μVrms and 7.5 μVrms ± 5.9 μVrms) for the time-averaged singledifferential and monopolar map reconstruction, respectively. Biharmonic Spline, the discrete cosine transform, and the Poisson PDE outperformed the other methods for the instantaneous map reconstruction. The running time of the proposed Poisson and wave PDE methods, implemented using a Vectorization package, was 4.6 ± 5.7ms and 0.6 ± 0.5ms, respectively, for each signal epoch or time sample in each channel. Conclusion: The proposed reconstruction algorithms could be promising new tools for reconstructing muscle activity maps in real-time applications. Significance: Proper reconstruction methods could recover the information of low-quality recorded channels in HDsEMG signals. © 2016 IEEE.
Publication Date: 2017
PLoS ONE (19326203) (12)
Publication Date: 2016
PLoS ONE (19326203) (12)
Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fullyautomatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies. © 2016 Marateb et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publication Date: 2016
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 233-238
The new generation of prosthetic devices are based on simultaneous and proportional estimation of kinematics from recorded surface electromyographic (sEMG) signals of the desire limb. In this paper we applied Generalized Regression Neural Network (GRNN), a non-linear system identification approach, to estimate fingers kinematics (15 Degrees of Freedom) from sEMG signals. The parameters were optimized based on training data of 40 subjects during 9 hand's principal movements. In order to reduce the input parameters of the model in a feature selection, suitable features such as auto regressive coefficients, zero crossing, slope sign change, waveform length, root mean square, and discrete wavelet transform were computed from sEMG signal. The performance of the estimation was assessed based on Pearson correlation coefficient or R-value index. The average overall Rvalue for 15 DoFs in all the subjects was 87.84±5.02%, comparable with the state of the art approaches in the literature. As the proposed method and set-up use dataglove to record kinematic information, thus has more realistic data acquisition protocol which has potential to be used in clinical setting to provide fast, accurate, and intuitive simultaneous and proportional control strategy for myoelectric hand prostheses. © 2015 IEEE.
Publication Date: 2016
Basic And Clinical Neuroscience (2008126X) (2)pp. 143-158
Neurofeedback is a kind of biofeedback, which teaches self-control of brain functions to subjects by measuring brain waves and providing a feedback signal. Neurofeedback usually provides the audio and or video feedback. Positive or negative feedback is produced for desirable or undesirable brain activities, respectively. In this review, we provided clinical and technical information about the following issues: (1) Various neurofeedback treatment protocols i.e. alpha, beta, alpha/theta, delta, gamma, and theta; (2) Different EEG electrode placements i.e. standard recording channels in the frontal, temporal, central, and occipital lobes; (3) Electrode montages (unipolar, bipolar); (4) Types of neurofeedback i.e. frequency, power, slow cortical potential, functional magnetic resonance imaging, and so on; (5) Clinical applications of neurofeedback i.e. treatment of attention deficit hyperactivity disorder, anxiety, depression, epilepsy, insomnia, drug addiction, schizophrenia, learning disabilities, dyslexia and dyscalculia, autistic spectrum disorders and so on as well as other applications such as pain management, and the improvement of musical and athletic performance; and (6) Neurofeedback softwares. To date, many studies have been conducted on the neurofeedback therapy and its effectiveness on the treatment of many diseases. Neurofeedback, like other treatments, has its own pros and cons. Although it is a non-invasive procedure, its validity has been questioned in terms of conclusive scientific evidence. For example, it is expensive, time-consuming and its benefits are not long-lasting. Also, it might take months to show the desired improvements. Nevertheless, neurofeedback is known as a complementary and alternative treatment of many brain dysfunctions. However, current research does not support conclusive results about its efficacy.
Publication Date: 2016
World Journal of Pediatrics (17088569) (3)pp. 335-342
Background: This study aimed to determine for the first time the age- and gender-specific reference intervals for biomarkers of bone, metabolism, nutrition, and obesity in a nationally representative sample of the Iranian children and adolescents. Methods: We assessed the data of blood samples obtained from healthy Iranian children and adolescents, aged 7 to 19 years. The reference intervals of glucose, lipid profile, liver enzymes, zinc, copper, chromium, magnesium, and 25-hydroxy vitamin D [25(OH)D] were determined according to the Clinical & Laboratory Standards Institute C28-A3 guidelines. The reference intervals were partitioned using the Harris–Boyd method according to age and gender. Results: The study population consisted of 4800 school students (50% boys, mean age of 13.8 years). Twelve chemistry analyses were partitioned by age and gender, displaying the range of results between the 2.5th to 97.5th percentiles. Significant differences existed only between boys and girls at 18 to 19 years of age for low density lipoprotein-cholesterol. 25(OH)D had the only reference interval that was similar to all age groups and both sexes. Conclusions: This study presented the first national database of reference intervals for a number of biochemical markers in Iranian children and adolescents. It is the first report of its kind from the Middle East and North Africa. The findings underscore the importance of providing reference intervals in different ethnicities and in various regions. © 2016, Children's Hospital, Zhejiang University School of Medicine and Springer-Verlag Berlin Heidelberg.
Publication Date: 2015
Journal Of Research In Medical Sciences (17351995) (3)pp. 214-223
Background: Coronary heart diseases/coronary artery diseases (CHDs/CAD), the most common form of cardiovascular disease (CVD), are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. Materials and Methods: In this study, the Cleveland CAD dataset from the University of California UCI (Irvine) was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC) whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS) methods, multiple logistic regression (MLR) and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS) was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. Results: In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard) for 272 subjects (68% male) were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α) and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were “age and ST/heart rate slope categories,” “exercise-induced angina status,” fluoroscopy, and thallium-201 stress scintigraphy results. Conclusion: The proposed method showed “substantial agreement” with the gold standard. This algorithm is thus, a promising tool for screening CAD patients. © 2015, Isfahan University of Medical Sciences(IUMS). All rights reserved.
Publication Date: 2015
Journal Of Medical Signals And Sensors (22287477) 5(4)pp. 245-252
In current years, the application of biopotential signals has received a lot of attention in literature. One of these signals is an electromyogram (EMG) generated by active muscles. Surface EMG (sEMG) signal is recorded over the skin, as the representative of the muscle activity. Since its amplitude can be as low as 50 μV, it is sensitive to undesirable noise signals such as power-line interferences. This study aims at designing a battery-powered portable four-channel sEMG signal acquisition system. The performance of the proposed system was assessed in terms of the input voltage and current noise, noise distribution, synchronization and input noise level among different channels. The results indicated that the designed system had several inbuilt operational merits such as low referred to input noise (lower than 0.56 μV between 8 Hz and 1000 Hz), considerable elimination of power-line interference and satisfactory recorded signal quality in terms of signal-to-noise ratio. The muscle conduction velocity was also estimated using the proposed system on the brachial biceps muscle during isometric contraction. The estimated values were in then normal ranges. In addition, the system included a modular configuration to increase the number of recording channels up to 96. © 2015 Journal of Medical Signals & Sensors.
Publication Date: 2014
Computers in Biology and Medicine (18790534) (1)pp. 34-42
Microalbuminuria (MA) is an independent predictor of cardiovascular and renal disease, development of overt nephropathy, and cardiovascular mortality in patients with type 2 diabetes. Detecting MA is an important screening tool to identify people with high risk of cardiovascular and kidney disease. The gold standard to detect MA is measuring 24-h urine albumin excretion. A new method for MA diagnosis is presented in this manuscript which uses clinical parameters usually monitored in type 2 diabetic patients without the need of an additional measurement of urinary albumin. We designed an expert-based fuzzy MA classifier in which rule induction was performed by particle swarm optimization. A variety of classifiers was tested. Additionally, multiple logistic regression was used for statistical feature extraction. The significant features were age, diabetic duration, body mass index and HbA1C (the average level of blood sugar over the previous 3 months, which is routinely checked every 3 months for diabetic patients). The resulting classifier was tested on a sample size of 200 patients with type 2 diabetes in a cross-sectional study. The performance of the proposed classifier was assessed using (repeated) holdout and 10-fold cross-validation. The minimum sensitivity, specificity, precision and accuracy of the proposed fuzzy classifier system with feature extraction were 95%, 85%, 84% and 92%, respectively. The proposed hybrid intelligent system outperformed other tested classifiers and showed "almost perfect agreement" with the gold standard. This algorithm is a promising new tool for screening MA in type-2 diabetic patients. © 2013 Elsevier Ltd.
Publication Date: 2014
Journal of Research in Medical Sciences (17357136) (1)pp. 47-56
Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal-variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables.
Publication Date: 2012
Bmc Pediatrics (14712431)
Background: The World Health Organization (WHO) is in the process of establishing a new global database on the growth of school children and adolescents. Limited national data exist from Asian children, notably those living in the Middle East and North Africa (MENA). This study aimed to generate the growth chart of a nationally representative sample of Iranian children aged 10-19 years, and to explore how well these anthropometric data match with international growth references.Methods: In this nationwide study, the anthropometric data were recorded from Iranian students, aged 10-19 years, who were selected by multistage random cluster sampling from urban and rural areas. Prior to the analysis, outliers were excluded from the features height-for-age and body mass index (BMI)-for-age using the NCHS/WHO cut-offs. The Box-Cox power exponential (BCPE) method was used to calculate height-for-age and BMI-for-age Z-scores for our study participants. Then, children with overweight, obesity, thinness, and severe thinness were identified using the BMI-for-age z-scores. Moreover, stunted children were detected using the height-for-age z-scores. The growth curve of the Iranian children was then generated from the z-scores, smoothed by cubic S-plines.Results: The study population comprised 5430 school students consisting of 2312 (44%) participants aged 10-14 years , and 3118 (58%) with 15-19 years of age. Eight percent of the participants had low BMI (thinness: 6% and severe thinness: 2%), 20% had high BMI (overweight: 14% and obesity: 6%), and 7% were stunted. The prevalence rates of low and high BMI were greater in boys than in girls (P < 0.001). The mean BMI-for-age, and the average height-for-age of Iranian children aged 10-19 years were lower than the WHO 2007 and United states Centers for Disease Control and Prevention 2000 (USCDC2000) references.Conclusions: The current growth curves generated from a national dataset may be included for establishing WHO global database on children's growth. Similar to most low-and middle income populations, Iranian children aged 10-19 years are facing a double burden of weight disorders, notably under- and over- nutrition, which should be considered in public health policy-making. © 2012 Mansourian et al.; licensee BioMed Central Ltd.
Publication Date: 2011
Journal of Neural Engineering (17412552) (6)
The aim of this study was to assess the accuracy of the convolution kernel compensation (CKC) method in decomposing high-density surface EMG (HDsEMG) signals from the pennate biceps femoris long-head muscle. Although the CKC method has already been thoroughly assessed in parallel-fibered muscles, there are several factors that could hinder its performance in pennate muscles. Namely, HDsEMG signals from pennate and parallel-fibered muscles differ considerably in terms of the number of detectable motor units (MUs) and the spatial distribution of the motor-unit action potentials (MUAPs). In this study, monopolar surface EMG signals were recorded from five normal subjects during low-force voluntary isometric contractions using a 92-channel electrode grid with 8 mm inter-electrode distances. Intramuscular EMG (iEMG) signals were recorded concurrently using monopolar needles. The HDsEMG and iEMG signals were independently decomposed into MUAP trains, and the iEMG results were verified using a rigorous a posteriori statistical analysis. HDsEMG decomposition identified from 2 to 30 MUAP trains per contraction. 3 ± 2 of these trains were also reliably detected by iEMG decomposition. The measured CKC decomposition accuracy of these common trains over a selected 10 s interval was 91.5 ± 5.8%. The other trains were not assessed. The significant factors that affected CKC decomposition accuracy were the number of HDsEMG channels that were free of technical artifact and the distinguishability of the MUAPs in the HDsEMG signal (P < 0.05). These results show that the CKC method reliably identifies at least a subset of MUAP trains in HDsEMG signals from low force contractions in pennate muscles. © 2011 IOP Publishing Ltd.
Publication Date: 2011
Journal of Neural Engineering (17412552) (6)
This paper presents a density-based method to automatically decompose single-channel intramuscular electromyogram (EMG) signals into their component motor unit action potential (MUAP) trains. In contrast to most previous decomposition methods, which require pre-setting and (or) tuning of multiple parameters, the proposed method takes advantage of the data-dependent strategies in the pattern recognition procedures. In this method, outliers (superpositions) are excluded prior to classification and MUAP templates are identified by an adaptive density-based clustering procedure. MUAP trains are then identified by a novel density-based classifier that incorporates MUAP shape and discharge time information. MUAP trains are merged by a fuzzy system that incorporates expert human knowledge. Finally, superimpositions are resolved to fill the gaps in the MUAP trains. The proposed decomposition algorithm has been experimentally tested on signals from low-force (≤30% maximal) isometric contractions of the vastus medialis obliquus, vastus lateralis, biceps femoris long-head and tibialis anterior muscles. Comparison with expert manual decomposition that had been verified using a rigorous statistical analysis showed that the algorithm identified 80% of the total 229 motor unit trains with an accuracy greater than 90%. The algorithm is robust and accurate, and therefore it is a promising new tool for decomposing single-channel multi-unit signals. © 2011 IOP Publishing Ltd.
Publication Date: 2005
Journal of Neuroscience Methods (1650270) (2)pp. 121-133
This paper describes an interactive computer program for decomposing EMG signals into their component motor-unit potential (MUP) trains and for averaging MUP waveforms. The program is able to handle single- or multi-channel signals recorded by needle or fine-wire electrodes during low and moderate levels of muscular contraction. It includes advanced algorithms for template matching, resolving superimpositions, and waveform averaging, as well as a convenient user interface for manually editing and verifying the results. The program also provides the ability to inspect the discharges of individual motor units more closely by subtracting out interfering activity from other MUP trains. Decomposition accuracy was assessed by cross-checking pairs of signals recorded by nearby electrodes during the same contraction. The results show that 100% accuracy can be achieved for MUPs with peak-to-peak amplitudes greater than 2.5 times the rms signal amplitude. Examples are presented to show how decomposition can be used to investigate motor-unit recruitment and discharge behavior, to study motor-unit architecture, and to detect action potential blocking in doubly innervated muscle fibers. © 2005 Elsevier B.V. All rights reserved.
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