Mehdizadeh dastjerdi, O.,
Bakhtiarnia, M.,
Yazdchi, M.,
Maghooli, K.,
Farokhi, F.,
Jadidi, K. Heliyon (24058440)9(9)
The common disorder, Keratoconus (KC), is distinguished by cumulative corneal slimming and steepening. The corneal ring implantation has become a successful surgical procedure to correct the KC patient's vision. The determination of suitable patients for the surgery alternative is among the paramount concerns of ophthalmologists. To reduce the burden on them and enhance the treatment, this research aims to previse the ocular condition of KC patients after the corneal ring implantation. It focuses on predicting post-surgical corneal topographic indices and visual characteristics. This study applied an efficacious artificial neural network approach to foretell the aforementioned ocular features of KC subjects 6 and 12 months after implanting KeraRing and MyoRing based on the accumulated data. The datasets are composed of sufficient numbers of corneal topographic maps and visual characteristics recorded from KC patients before and after implanting the rings. The visual characteristics under study are uncorrected visual acuity (UCVA), sphere (SPH), astigmatism (Ast), astigmatism orientation (Axe), and best corrected visual acuity (BCVA). In addition, the statistical data of multiple KC subjects were registered, including three effective indices of corneal topography (i.e., Ast, K-reading, and pachymetry) pre- and post-ring embedding. The outcomes represent the contribution of practical training of the introduced models to the estimation of ocular features of KC subjects following the implantation. The corneal topographic indices and visual characteristics were estimated with mean errors of 7.29% and 8.60%, respectively. Further, the errors of 6.82% and 7.65% were respectively realized for the visual characteristics and corneal topographic indices while assessing the predictions by the leave-one-out cross-validation (LOOCV) procedure. The results confirm the great potential of neural networks to guide ophthalmologists in choosing appropriate surgical candidates and their specific intracorneal rings by predicting post-implantation ocular features. © 2023 The Authors
In this article, a new framework is proposed to address multi-class Motor Imagery Brain-Computer Interface (MIBCI) problems containing a small portion of labeled datasets. In this framework, the combination of Independent Component Analysis (ICA), multi-class Common Spatial Pattern (CSP), and a functional Application Programming Interface (API) model assumes a pivotal role. In the feature extraction stage of the work, a concatenated altered signal affected by spatial weights is proposed for each trial in three frequency ranges. This distribution of features can both provide suitable feature maps for augmentation, preparing data for the deep learning analysis, and underscore distinguishable features of MI classes. In the classification stage, spatial and temporal features are dominated by using the effective combination of a one-dimensional Convolutional Neural Network (CNN) and a two-staged Bidirectional Long Short-Term Memory (BLSTM) in three branches containing different distributions of frequency. Given that, the model simultaneously learns past-tofuture and future-to-past patterns in two stages. The experimental result on datasets 2a BCI-Competition IV illustrates that the proposed method can be liable, practical and more competitive than the other popular methods pointed out in this paper. All in all, the proposed framework can alleviate the issue of small portions of labeled datasets in MI problems.
Biomedical Signal Processing and Control (17468108)78
Proposing a practical method for high-performance emotion recognition could facilitate human–computer interaction. Among existing methods, deep learning techniques have improved the performance of emotion recognition systems. In this work, a new multimodal neural design is presented wherein audio and visual data are combined as the input to a hybrid network comprised of a bidirectional long short term memory (BiLSTM) network and two convolutional neural networks (CNNs). The spatial and temporal features extracted from video frames are fused with Mel-Frequency Cepstral Coefficients (MFCCs) and energy features extracted from audio signals and BiLSTM network outputs. Finally, a Softmax classifier is used to classify inputs into the set of target categories. The proposed model is evaluated on Surrey Audio–Visual Expressed Emotion (SAVEE), Ryerson Audio–Visual Database of Emotional Speech and Song (RAVDESS), and Ryerson Multimedia research Lab (RML) databases. Experimental results on these datasets prove the effectiveness of the proposed model where it achieves the accuracy of 99.75%, 94.99%, and 99.23% for the SAVEE, RAVDESS, and RML databases, respectively. Our experimental study reveals that the suggested method is more effective than existing algorithms in adapting to emotion recognition in these datasets. © 2022 Elsevier Ltd
In this article, a new semi-automatic Electroencephalogram (EEG) artifact removal has been proposed for Motor Imagery (MI) tasks to improve the system performance. There are eight reference clusters whose locations have been calculated based on the precise coordinates of a copious amount of brain dipoles acquired from a large number of users performing MI tasks. In this method, called 8 Ref-Clusters, a kind of Blind Source Separation (BSS) algorithm along with the DIPFIT plugin of the EEGLAB platform take on a decisive role. These eight clusters demonstrate which dipoles are brain sources and which ones are artifacts to eliminate. In the case of improving the performance of a system for a particular subject, we defined a specific threshold that could alter the size of clusters in three dimensions. The elaborate threshold pointed out above is unquestionably user-dependent. Having made a comparison between results before and after applying the 8-Ref-Clusters on the BCI-Competition IV 2a datasets, the average performance increased by roughly 4% which is promising when the datasets used in these evaluations had been filtered, between 8 and 30 Hz, only before applying Independent Component Analysis (ICA). Making a comparison between the results of the proposed method and those of other CSP-based methods shows that applying the proposed artifact removal only before CSP can significantly enhance the performance of the system at about 15.6%, in the case of the mCSP method. All in all, the proposed artifact removal method is a semi-automatic method that is computationally fast and able to detect the various types of artifacts like a heartbeat, muscle and head movements, eye blink, line noise, and so on.
Journal Of Medical Signals And Sensors (22287477)12(2)pp. 114-121
Background: One of the most prevalent methods in noninvasive blood pressure (BP) measurement with cuff is oscillometric, which has two different types of deflation, including linear and step deflation. With this approach, in addition to designing a novel algorithm by the step deflation method, a sample of its module was constructed and validated during clinical tests in different hospitals. Method: In this study, by controlling the valve, the pressure would be deflated through optimized steps. By real-Time processing on the obtained signal from the pressure sensor, pulses in each step would be extracted. After that, in offline mode, mean arterial pressure is estimated based on curve fitting. Result: A BP simulator, various modules, and an auditory method were used to validate the algorithm and its results. During clinical tests, 80 people (men and women), 11 dialysis patients, and 69 non-dialysis (healthy or with other diseases) in the age range of 17-85 years participated. Conclusion: The obtained results compared with the BP simulator are in the standard range according to the international medical standards of the British Hypertension Society (BHS) and the US Association for the Advancement of Medical Instrumentation (AAMI), which are the global standard of comparison in this field. © 2022 Isfahan University of Medical Sciences(IUMS). All rights reserved.
PLoS ONE (19326203)17(2 February)
Differentiating between shockable and non-shockable Electrocardiogram (ECG) signals would increase the success of resuscitation by the Automated External Defibrillators (AED). In this study, a Deep Neural Network (DNN) algorithm is used to distinguish 1.4-second segment shockable signals from non-shockable signals promptly. The proposed technique is frequency-independent and is trained with signals from diverse patients extracted from MIT-BIH, MIT-BIH Malignant Ventricular Ectopy Database (VFDB), and a database for ventricular tachyarrhythmia signals from Creighton University (CUDB) resulting, in an accuracy of 99.1%. Finally, the raspberry pi minicomputer is used to load the optimized version of the model on it. Testing the implemented model on the processor by unseen ECG signals resulted in an average latency of 0.845 seconds meeting the IEC 60601-2-4 requirements. According to the evaluated results, the proposed technique could be used by AED’s. Copyright: © 2022 Nasimi, Yazdchi. 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.
Biomedical Signal Processing and Control (17468108)70
Objective: With deepened interactions between human and computer, the need for a reliable and practical system for emotion recognition has become significant. The aim of this study is to propose a practical system for estimation of a continuous measure of valence based on a few number of EEG channels. Methods: A vast spectrum of time, frequency and coherence features were implemented with linear Regression (LR), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) models and then ranked for the performance on DEAP database using a regression-based Relief filter. Regression outcomes were also classified to compare the performance of the proposed method with the literature. Finally, a video-based emotion recognition experiment was designed and conducted on 12 subjects using F7, F8, FC2 and T7 electrodes. Results: Magnitude Squared Coherence Estimate(MSCE) on F7–F8 with SVR model provided the highest performance on DEAP dataset. Classification of the output led to an average accuracy of 67.5%. For the gathered data, combination of MSCE and Hilbert–Huang Spectrum provided the best performance with 0.22 root mean square error and 0.67 correlation with self-reported valence in the scale of 1–9. Conclusion: MSCE could provide a good accuracy in estimation of Valence using 2 EEG channels on Deep dataset, and with addition of Hilbert–Huang Spectrum, it also demonstrated good accuracy and correlation with self-reported valence, in a completely different experiment. Significance: Continuous-value estimation of the valence can be achieved with only 2 EEG channels for practical applications out of the lab. © 2021 Elsevier Ltd
Journal Of Medical Signals And Sensors (22287477)10(1)pp. 53-59
Obstructive sleep apnea (OSA) is a common disorder which can cause periodic fluctuations in heart rate. To diagnose sleep apnea, some studies analyze electrocardiogram (ECG) signals by adopting chaos-based analysis. This research is going to specifically focus on whether it is possible to use chaos-based analysis of heart rate variability (HRV) signals rather than using chaotic analysis of ECG signals to diagnose OSA. While conventional studies mostly use chaos-based analysis of ECG signals to detect OSA, here, we apply correlation dimension (CD) as a chaotic index to analyze HRV data in OSA patients. For this purpose, 17 patients with OSA and 9 healthy individuals referred to a sleep clinic in Isfahan/Iran are studied, and their HRV time series were extracted from 1-h ECG signals recorded overnight. The preliminary step to calculate CD is phase-space reconstruction of the system based on HRV time series. Corresponding parameters, including embedding dimension and lag time, are estimated optimally using enhanced related methods, and then CD is calculated using Grassberger-Procaccia algorithm. Moreover, to evaluate our results, detrended fluctuation analysis (DFA), one of the well-known nonlinear methods in HRV analysis to detect OSA, is also applied to our data and the result is compared with those obtained from CD analysis of HRV. CD index with P < 0.005 indicates a significant difference in nonlinear dynamics of HRV signals detected from OSA patients and healthy individuals. © 2020 Isfahan University of Medical Sciences(IUMS). All rights reserved.
Microelectronic Engineering (01679317)216
This paper presents an ultra-low-power CMOS amplifier for Low-frequency biosignal recording applications, especially for implantable biosensors and wearable or portable devices such as wearable electrocardiogram (ECG) recording microsystems that power consumption and on-chip area are the critical constraints. The proposed amplifier consists of two stages. In the first stage, a current reuse topology is used to achieve a lower input-referred voltage noise power density compared to a normal single-ended common source amplifier. In order to reduce the on-chip area, an inverter based amplifier with high output impedance is used at the output stage of the circuit to reduce the area occupied by the large load capacitance, which is required for having low cut off frequency of 200 Hz. Using this technique, the required load capacitance is decreased to about 2.1 pF. To reduce the power consumption, a ± 0.6 V supply voltage is used, and the transistors are pushed toward sub-threshold region to reduce the bias currents to about 260 nA in the first and the second stages. To validate the design, the proposed amplifier is simulated using 0.18 μm TSMC CMOS technology with Cadence in post-layout level. It is shown that the total power consumption of the proposed amplifier is as low as 640 nW. The proposed amplifier benefits from a CMRR >125 dB, total input-referred noise of 6.1 μVrms in 200 Hz bandwidth for ECG signal recording, and a closed-loop gain of about 35.1 dB. The layout of the circuit occupies a total area of 0.034 mm2. © 2019 Elsevier B.V.
Khayamnia, M.,
Yazdchi, M.,
Heidari, A.,
Foroughipour, M. Journal Of Medical Signals And Sensors (22287477)9(3)pp. 174-180
Background: Headache is one of the most common forms of medical complaints with numerous underlying causes and many patterns of presentation. The first step for starting the treatment is the recognition stage. In this article, the problem of primary and secondary headache diagnosis is considered, and we evaluate the use of intelligence techniques and soft computing in order to predict the diagnosis of common headaches. Methods: A fuzzy expert-based system for the diagnosis of common headaches by Learning-From-Examples (LFE) algorithm is presented, in which Mamdani model was used in fuzzy inference engine using Max-Min as Or-And operators, and the Centroid method was used as defuzzification technique. In addition, this article has analyzed common headache using two classification techniques, and headache diagnosis based on a support vector machine (SVM) and multilayer perceptron (MLP)-based method has been proposed. The classifiers were used to recognize the four types of common headache, namely migraine, tension, headaches as a result of infection, and headaches as a result of increased intra cranial presser. Results: By using a dataset obtained from 190 patients, suffering from primary and secondary headaches, who were enrolled from a medical center located in Mashhad, the diagnostic fuzzy system was trained by LFE algorithm, and on an average, 123 pieces of If-Then rules were produced for fuzzy system, and it was observed that the system had the ability of correct recognition by a rate of 85%. Using the headache diagnostic system by MLP- and SVM-based decision support system, the accuracy of classification into four types improved by 88% when using the MLP and by 90% with the SVM classifier. The performance of all methods is evaluated using classification accuracy, precision, sensitivity, and specificity. Conclusion: As the linguistic rules may be incomplete when human experts express their knowledge, and according to the proximity of common headache symptoms and importance of early diagnosis, the LFE training algorithm is more effective than human expert system. Favorable results obtained by the implementation and evaluation of the suggested medical decision support system based on the MLP and SVM show that intelligence techniques can be very useful for the recognition of common headaches with similar symptoms. © 2019 Journal of Medical Signals & Sensors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (1557170X)pp. 302-304
Patients with implantable cardioverter-defibrillator (ICD) are at the risk of electrical storm (ES) occurrence associated with mortality and poor quality of life. Cardiac resynchronization therapy with defibrillator (CRT-D) minimizes inappropriate ICD shocks. However, limited reports exist on the impact of CRT-D versus traditional ICD on ES occurrences in real-life cohorts. We evaluated the implanted-device characteristics associated with ES events in a large data based on daily stored device-summaries obtained from remote monitoring data in US.Between 2004 and 2016, 19,935 US patients were implanted. Survival analyses with Cox regression for device-shock therapy were performed between patients who experienced at least one ES and those without ES. CRT-D devices (bi-ventricular) were implanted in 5522 (28%) patients during this period, and their ES events over time were compared to ICD recipients implanted with RV lead. Primary endpoint was the first ES event.ES occurred with the rate of 7.26% for all patients during the period. Cox regression analyses revealed significantly an increase risk in ES occurrences (the p-value < 0.05 and hazard ratio >> 1) with shock therapy. CRT-D implant led to lower ES risk comparing with patients received traditional ICD (RV only). © 2019 IEEE.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (1557170X)pp. 4885-4888
Electrical storm (ES) is a life-threatening heart condition for patients with implantable cardioverter defibrillators (ICDs). ICD patients experienced episodes are at higher risk for ES. However, predicting ES using previous episodes' parameters recorded by ICDs have never been developed. This study aims to predict ES using machine learning models based on ICD remote monitoring-summaries during episodes in the anonymized large number of patients.Episode ICD-summaries from 16,022 patients were used to construct and evaluate two models, logistic regression and random forest, for predicting the short-term risk of ES.Episode parameters in this study included the total number of sustained episodes, shocks delivered and the cycle length parameters. The models evaluated on the data sections not used for model development.Random forest performed significantly better than logistic regression (P < 0.01), achieving a test accuracy of 0.99 and an Area Under an ROC Curve (AUC) of 0.93 (vs. an accuracy of 0.98 and an AUC of 0.90). The total number of previous sustained episodes was the most relevant variables in the both models. © 2019 IEEE.
Signal, Image and Video Processing (18631711)11(6)pp. 1009-1016
Intravascular ultrasound (IVUS) is clinically available for visualizing coronary arteries. However, it suffers from acoustic shadow areas and ring-down artifacts as two of the common issues in IVUS images. This paper introduces an approach which can overcome these limitations. As shadow areas were displayed behind hard plaques in the IVUS grayscale images, calcified plaques were first segmented by using Otsu threshold. Then, active contour, histogram matching, and local histogram matching are implemented. In addition, a new modified circle Hough transform is introduced to remove the ring-down artifacts from IVUS images. In order to evaluate the efficacy of this new method in detection of shadow and ring-down regions, 300 IVUS images are considered. Sensitivity of 89% and specificity of 92% are achieved from a comparison in revelation of calcium along with shadow in the proposed method and virtual histology images. Also, area differences of 5.83 ± 3.3 and 5.65 ± 2.83 are obtained, respectively, for ring-down and shadow domain when compared to measures performed manually by a clinical expert. © 2017, Springer-Verlag London.
Anvari, S.M.,
Yazdchi, M.,
Kayvanpour, A.,
Nayebpour, S.M.H.,
Koivisto, T.,
Tadi, M.J. Computing in Cardiology (2325887X)44pp. 1-4
Hypertension or high blood pressure (BP) is one of the most common worldwide disease leading to heart attack or stroke. Continuous assessment of blood pressure level is key to diagnosing hypertension. In this study, we designed and tested a dedicated cuff-less monitoring system which estimates BP level without need for calibration. We obtained continuous measurements from 40 healthy subjects (30 males and 10 females) ranging from 20-30 years old. Our measurement protocol consisted of 15 minutes simultaneous electrocardiography (ECG) and photoplethysmography (PPG) within three sessions, i.e. rest, bicycle exercise, and recovery. From ECG and PPG signals, we obtained 34 candidate features from which up to 9 features were selected to estimate systolic and diastolic BP levels. We validate our results with three regression models such as linear regression, support vector machines (SVM) regression, and multilayer perceptron (MLP) to obtain the best results. The study provides a promising approach for modern cuff-less BP monitoring devices. © 2017 IEEE Computer Society. All rights reserved.
Yazdchi, M.,
Khayamnia, M.,
Vahidiankamyad A.,
Foroughipour, M.,
Khayamnia, M.,
Khayamnia, M.,
Khayamnia, M.,
Yazdchi, M.,
Yazdchi, M.,
Yazdchi, M.,
Vahidiankamyad A.,
Vahidiankamyad A.,
Vahidiankamyad A.,
Foroughipour, M.,
Foroughipour, M.,
Foroughipour, M. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 50-53
The migraine headache is a kind of most populated headache which its rate of population is so high. The first step for starting of treatment is the recognition stage. Also the fuzzy logic has good power for describing of enigmatic and imprecise aspects and due to this reason this tool could be used for the system modeling. The aim of this research is the migraine recognition by the usage of fuzzy logic and systems. A fuzzy expert system for diagnosis of migraine by LFE algorithm is presented, that Mamdani model was used in fuzzy inference engine using MAX-MIN as OR-AND operators and Centroid method was used as defuzzification technique. By the usage of 148 patients, the migraine diagnostic system has been trained by LFE algorithm and in average 80 pieces of IF-THEN rules have been produced for fuzzy system and accuracy, precision, sensitivity, specificity of the system were 97%, 80%, 70%, 94%. By attention to this point that the linguistic rules may be incomplete when human expert to express their knowledge and according to importance of early diagnosis and favorable results, the LFE training algorithm rather than human experts system, will be more effective for recognition of migraine headache. © 2017 IEEE.
Journal of Isfahan Medical School (10277595)34(416)pp. 1680-1685
Background: The migraine headache is a kind of most populated headache which its prevalence rate is so high. The first step for starting the treatment is the recognition stage. In addition, the fuzzy logic has good power for describing enigmatic and imprecise aspects; so, this tool could be used for the system modeling. This research aimed to recognize the migraine via using fuzzy logic and systems. Methods: A fuzzy expert system for diagnosis of migraine via Learning from Examples (LFE) algorithm was presented. Mamdani model was used in fuzzy inference engine using Max-Min as Or-And operators and Centroid method was used as defuzzification technique. Findings: Using the data of 148 patients, the migraine diagnostic system was trained by LFE algorithm and in average, 80 pieces of If-Then rules were produced for fuzzy system. The accuracy, precision, sensitivity, and specificity of the system were 97%, 80%, 70%, and 94%, respectively. Using the migraine diagnostic system by human experts, it was proved that the system had the ability of correct recognition by the rate of 81%. Conclusion: As the linguistic rules may be incomplete when human expert express their knowledge and according to importance of early diagnosis and favorable results, the LFE training algorithm is more effective than human experts system for recognition of migraine headache. © 2017, Isfahan University of Medical Sciences(IUMS). All rights reserved.
Indian Journal of Science and Technology (discontinued) (09746846)9(16)
Neural micro-probing not only allows us to access the brain signals, it is also one of the key tools to achieve a better understanding of the fundamental mechanisms of the brain and nervous system. This knowledge is extremely essential to implement complex neuralprosthetics. Considering the importance of the size, power consumption, and accuracy, in this Paper, a compact, low-power eight-channel implantable neural recording microsystem is presented. To this end, Time Division Multiplexing (TDM) method is used for multiplexing 8 input channels. Thus, only one Analog to Digital Converter (ADC) block is used, resulting in a small circuit size and low power consumption. Also, a new structure of Amplitude-Shift Keying (ASK) modulation technique with simple configuration and low power consumption is proposed and applied. This system is designed in TSMC 0.18 μm CMOS technology with 1.8 V power supply and simulated with HSPICE. The total power consumption of the system is measured to 2 mW. Neural signals recorded from auditory cortex of a guinea pig are used as the inputs of the eight channels of the system. Total RMS error between inputs and outputs caused by the system is 2.8%.
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.
Hajrasooliha, M.,
Mohammadbeigi, M.,
Yazdchi, M.,
Khani, B. Journal of Isfahan Medical School (10277595)33(335)
Background: Neonatal jaundice is a common disease, and has been seen in almost 60% of term and 80% of preterm infants. There are three methods of assessing bilirubin level, visual, cutaneous and serum measurement. Method of measuring serum bilirubin, due to blood sampling from the baby, is not ideal. Visual assessment method is not an accurate criterion. So, the researchers looked for a way to measure bilirubin level non-invasively. In 1980, the first review was done and high correlation was found between the skin bilirubin level and the amount of bilirubin in serum. Some of noninvasive bilirubinmeter devices poorly acted in heterogeneous groups in terms of gestational age and races. Some of the newer methods, for each infant, need an initial correction. The latest method of measuring skin bilirubin uses the reflection of the wide spectrum of visible light. Methods: We intended the designing and construction of a noninvasive neonatal jaundice meter, using newest methods and designs. In this device, light, with five different wavelengths, transmitted to the skin and after measuring the reflection from baby’s skin, the bilirubin concentration in terms of mg/dl could be obtained. Findings: Clinical testing of the device was done on 32 infants; and correlation of 74%, between the transcutaneous (TCB) and the total serum bilirubin (TSB) values was obtained. Conclusion: An acceptable correlation was obtained between the transcutaneous and the total serum bilirubin values. The device can be used to screen newborns for measurement of bilirubin with decreased number of blood samples. © 2015, Isfahan University of Medical Sciences(IUMS). All rights reserved.
Karimizadeh a., ,
Mahnam, A.,
Yazdchi, M.,
Besharat m.a., Journal of Psychophysiology (21512124)29(3)pp. 107-111
During the last decade, an increasing number of studies have used neuroscientific methods to examine the relationships between different personality traits and brain structures. This includes the Magnetic Resonance Imaging (MRI)-based analysis of correlations between individual differences in personality traits and the structural variance of specific brain regions. Perfectionism is a personality trait that remains relatively stable over time, and it is influenced by heredity. In this study, the possible brain regions that structurally correlated with both positive and negative perfectionism were investigated. Voxel-based morphometry was used to analyze the whole brain MRI images of 49 participants, and their levels of perfectionism were also evaluated using a standard scale. The statistical analysis revealed significant correlations between negative perfectionism and the gray matter volume of the thalamus and left posterior parietal cortex (precuneus) structures. This finding suggests that differences in perfectionism between individuals may reflect structural variances in these regions of the brain. © 2015 Hogrefe Publishing.
Middle East Conference on Biomedical Engineering, MECBME (21654247)pp. 309-312
The goal of image enhancement is improving the interpretability or perception of information in images for human viewers. This paper describes, an automated algorithm for shadow region detection and enhancement in intravascular ultrasound (IVUS) images using an adaptive threshold method for threshold selection, contour approach for border detection and image enhancement algorithm including histogram analysis for the shadow regions improvement. As shadow appears behind the calcification plaque, it makes it difficult or impossible for the dark region to process automatically around these regions. The acoustic shadow usually follows the hard plaque in IVUS images and it can distinguish calcification regions from other bright regions. Therefore we propose to use Otsu Threshold for calcification plaque segmentation and the Active contours without edge method for shadow region separation of the image and histogram matching for shadow enhancing. Results show that the proposed method efficiently detected shadow regions even in complicated images. © 2014 IEEE.
Yazdchi, M.,
Karimimehr, S.,
Karimimehr, S.,
Karimimehr, S.,
Karimimehr, S.,
Yazdchi, M.,
Yazdchi, M.,
Yazdchi, M. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 410-413
Computational neuroscience is a growing discipline in science, which tries to understand the operations of human brain and inspire from it as a new computational paradigm. Face recognition is an important question both in pattern recognition and neuroscience. In the last few years, neuroscientists found many facts about object recognition in primate's brain. Here, we propose a cortex inspired face recognition system which uses some findings about the brain such as the operations of feature extractor cells in visual cortex and the function of attention in discarding distracting parts of the images. Now it is the time to merge the knowledge of learning systems with biological findings. The proposed method named Advanced Neurologically Inspires Face recognition (ANIF) system is compared with previous model NIF and some well-known face recognition algorithms within different datasets, which shows remarkable results. © 2014 IEEE.
Parastar-feizabadi, M.,
Yazdchi, M.,
Ghoshuni, M.,
Hashemian, P. Journal of Isfahan Medical School (10277595)32(283)pp. 558-568
Background: Electroencephalogram (EEG) shows the electrical activity of the brain and is one of the most important diagnostic tools for neurological diseases and disabilities. Dysgraphia is one of the most common learning disabilities occurs regardless of the ability to read and is not due to intellectual impairments. Nonlinear methods are used in recent studies to access the electroencephalogram in children with dysgraphia.
Jazi, M.H.,
Amra, B.,
Yazdchi, M.,
Jahangiri, M.,
Tabesh, F.,
Gholamrezaei, A. Sleep and Breathing (15221709)18(3)pp. 549-554
Purpose: The underlying mechanisms of the association between obstructive sleep apnea (OSA) and atrial fibrillation (AF) remained unclear. We investigated P wave parameters as indicators of atrial conduction status among OSA patients.
Iranian Journal of Medical Physics (17357241)10(3)pp. 139-146
Introduction: In CT imaging, metallic implants inside the tissues cause metal artifact that reduce the quality of image for diagnosis. In order to reduce the effect of this artifact, a new method with more appropriate results has been presented in this research work. Materials and Methods: The presented method comprised of following steps: a) image enhancement and metal areas extraction, b) sinogram transform of original image, c) metal segments and metal traces inside the sinogram transform of original image segmented by using Fuzzy C means, d) interpolation of metal traces inside the original image sinogram and filtering, and e) adding the image of metal parts to the filtered image to obtain the corrected image. Results: Fifty CT scan images from Alzahra Hospital in Isfahan were used to evaluate the proposed method. The proposed method was applied to images which had implants in regions such as femur, hip, tooth, brain, and stomach. The results showed an intensively reduced in metal artifact and quality improvement of images till 90% for accuracy, compared with the radiologist report. Conclusion: The proposed method reduced the effect of metal artifact by maintaining the specification of other tissues. Furthermore, the consumed time to process the suggested algorithm in this study was less than conventional methods. For instance, the consumed time for CT image, including a metal in the femur region was about 20% of the conventional method.
Journal of Electrical Engineering and Technology (19750102)8(6)pp. 1487-1496
In this paper, a novel algorithm for PD localization in power transformers based on wavelet de-noising technique and energy criterion is proposed. Partial discharge is one of the main failures in power transformers. The localization of which could be very useful for maintenance systems. Acoustic signals due to a PD event are transient, irregular and non-repetitive. So wavelet transform is an efficient tool for this signal processing problem that gives a time-frequency demonstration. First, different wavelet based de-noising methods are analyzed. Then, a reasonable structure for threshold value determining and applying manner on signals is presented. Evaluated errors are good evidences for choices. Next, applying the elimination low energy frequency bands is discussed and developed as a de-noising method. Time differences between signals are used for PD localization. Different ways in time arrival detection are introduced and a novel approach in energy criterion method is presented. At the end, the quality of algorithm is verified through the different assays in lab.
Yazdchi, M.,
Yazdanian H.,
Nomani A.,
Yazdanian H.,
Yazdanian H.,
Yazdanian H.,
Nomani A.,
Nomani A.,
Nomani A.,
Yazdchi, M.,
Yazdchi, M.,
Yazdchi, M. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 223-227
In this paper a new method for categorizing 5 special types of heartbeats has been developed by use of time and apparent properties of the Wavelet Transform of the ECG signal. By using the method in this paper first each heart beat identified autonomously and important points and segments of it, were derived. Then expected features for categorizing the heartbeats are extracted. Finally we categorized the arrhythmias by using the Support Vector Machines. In order to train the SVM and for analyzing its accuracy; arrhythmic signals of MIT-BIH dataset have been used. The results which have been achieved by this method also contain 96.67 percent of accuracy for categorizing five different heartbeats including Normal (N) Left Bundle Branch Block(LBBB), Right Bundle Branch Block(LBBB), Premature Ventricular Contraction (PVC) and Atrial Premature Contraction (APC). The advantage of using this method compared to the other ones is that we could achieve the expected precision by using less training attributes respect to the other methods. © 2013 IEEE.
Yazdchi, M.,
Tork S.,
Karimizadeh a., ,
Tork S.,
Tork S.,
Tork S.,
Yazdchi, M.,
Yazdchi, M.,
Yazdchi, M.,
Karimizadeh a., ,
Karimizadeh a., ,
Karimizadeh a., 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025
Sleeping is an important part of human's life because of its state of unconsciousness and relaxation. According to today's common problems, such as increased risks of social, economic and healthcare, disturbances or interruptions in breathing pattern may occur during sleep or it may be discontinued that knows as Sleep Disorders Breath(SDB). Thus, the researchers used an abnormal breathing pattern and characteristics of sleep to detect disorder and help these patients. In this way without any extra equipment, using ECG-Derived Respiration from Electrocardiogram can be a noninvasive, low-cost measurement of respiration wave. In this research, three major methods reinvestigate and the accurate method with the accuracy of 0.9 selected. EDR can be classified by a linear discriminate classification to normal or abnormal (apnea) epoch. In conclusion part, three methods were compared and by selecting the more accurate one, EDR epoch were classified by more than 79.66% accuracy. © 2013 IEEE.
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (1062922X)pp. 2173-2177
This paper describes an automated algorithm for shadow region detection in Intra Vascular Ultrasound images, using an adaptive threshold method for threshold selection and contour approach for border detection. As shadow appears behind the calcification plaque, it makes it difficult or impossible for the dark region to process automatically aroundf these regions. The acoustic shadow usually follows the hard plaque in IVUS images and it can distinguish calcification regions from other bright regions. Therefore we propose to use Otsu Threshold for calcification plaque segmentation and the Active contours without edge method for shadow region separation of the image. Results show that the proposed meth efficiently detected shadow regions even in complicated images. This proposed algorithm presented specificity of 86% and sensitivity of 93%. © 2012 IEEE.
Forouharmajd, F.,
Nassiri, P.,
Monazzam, M.R.,
Yazdchi, M. Noise and Health (14631741)14(59)pp. 135-139
Noise may be defined as any unwanted sound. Sound becomes noise when it is too loud, unexpected, uncontrolled, happens at the wrong time, contains unwanted pure tones or unpleasant. In addition to being annoying, loud noise can cause hearing loss, and, depending on other factors, can affect stress level, sleep patterns and heart rate. The primary object for determining subjective estimations of loudness is to present sounds to a sample of listeners under controlled conditions. In heating, ventilation and air conditioning (HVAC) systems only the ventilation fan industry (e.g., bathroom exhaust and sidewall propeller fans) uses loudness ratings. In order to find satisfaction, percent of exposure to noise is the valuable issue for the personnel who are working in these areas. The room criterion (RC) method has been defined by ANSI standard S12.2, which is based on measured levels of in HVAC systems noise in spaces and is used primarily as a diagnostic tool. The RC method consists of a family of criteria curves and a rating procedure. RC measures background noise in the building over the frequency range of 16-4000 Hz. This rating system requires determination of the mid-frequency average level and determining the perceived balance between high-frequency (HF) sound and low-frequency (LF) sound. The arithmetic average of the sound levels in the 500, 1000 and 2000 Hz octave bands is 44.6 dB; therefore, the RC 45 curve is selected as the reference for spectrum quality evaluation. The spectral deviation factors in the LF, medium-frequency sound and HF regions are 2.9, 7.5 and-2.3, respectively, giving a Quality Assessment Index (QAI) of 9.8. This concludes the QAI is useful in estimating an occupant′s probable reaction when the system design does not produce optimum sound quality. Thus, a QAI between 5 and 10 dB represents a marginal situation in which acceptance by an occupant is questionable. However, when sound pressure levels in the 16 or 31.5 Hz octave bands exceed 65 dB, vibration in lightweight office construction is possible.
Research Journal of Applied Sciences, Engineering and Technology (discontinued) (20407459)4(8)pp. 852-858
Power System Stabilizers (PSS) are used to generate supplementary damping control signals for the excitation system in order to damp the Low Frequency Oscillations (LFO) of the electric power system. The PSS is usually designed based on classical control approaches but this Conventional PSS (CPSS) has some problems in power system control and stability enhancement. To overcome the drawbacks of CPSS, numerous techniques have been proposed in literatures. In this study a new method based on Model Reference Robust Fuzzy Control (MRRFC) is considered to design PSS. In this new approach, in first an optimal PSS is designed in the nominal operating condition and then power system identification is used to obtain model reference of power system including optimal PSS. With changing system operating condition from the nominal condition, the error between obtained model reference and power system response in sent to a fuzzy controller and this fuzzy controller provides the stabilizing signal for damping power system oscillations just like PSS. In order to model reference identification a PID type PSS (PID-PSS) is considered for damping electric power system oscillations. The parameters of this PID-PSS are tuned based on hybrid Genetic Algorithms (GA) optimization method. The proposed MRRFC is evaluated against the CPSS at a single machine infinite bus power system considering system parametric uncertainties. The simulation results clearly indicate the effectiveness and validity of the proposed method. © Maxwell Scientific Organization, 2012.
Fathi kazerooni a., A.F.,
Rabbani m., M.,
Yazdchi, M.,
Kasiri, S.,
Rad, H.S. Biomedizinische Technik (1862278X)56(3)pp. 167-173
This paper presents a new modification to the previous model of bone surface remodeling under electric and magnetic loadings. For this study, the thermo-electro-magneto-elastic model of bone surface remodeling is used. This model is modified by considering an important phenomenon occurring in living bone through its adaptation to external loadings called desensitization. In fact, bone cells lose their responsiveness and sensitivity to long-term external loadings, i.e., they become desensitized. Therefore, bone cells need a recovery period, during which they become resensitized. In this work, this phenomenon is considered in the original model. The effects of various electric and magnetic loading conditions, including various frequencies, waveforms and pulse duty cycles, are explored on the modified model and compared to the original model. The modified model is also searched for the optimal frequency and duty cycle, to obtain the best bone growth response under electromagnetic fields. The results of this paper show that the modified model is consistent with experimental observations. In addition, it is indicated that this modified model in contrast to the original model, is sensitive to frequency. It is shown that the optimal frequency of loading for the modified model is 1 Hertz (Hz), and the pulse duty cycles up to 50% are sufficient for bone remodeling to reach its maximum value. © 2011 by Walter de Gruyter Berlin Boston.
Physics Procedia (18753892)22pp. 209-211
Metal artifact is one of the major problems in CT images. To overcome this problem a new algorithm has been suggested in this research. This algorithm has five steps means: a) Extraction the metal region from the image, b) Filtration the extracted metallic region, c) Segmentation and accurate extraction of metallic pieces by FCM (Fuzzy C Means) method, d) Using the interpolation on the sinogram and finally e) Insert the corrected metallic section of image to the original image. The results of this research showed, by using this new algorithm the output image has better contrast, signal to noise ratio and visibility with much less consumed time than the other reported methods. © 2011 Published by Elsevier B.V.
IFMBE Proceedings (16800737)35pp. 458-462
Objective: During recent years, a great deal of research has been done for finding methods to prevent, diagnose and heal serious diseases of bone such as osteoporosis. Among the methods proposed, exogenous mechanical, thermal, electric and magnetic stimulation have been of more interest due to the noninvasiveness and efficiency beside costeffectiveness. In fact, it is necessary to obtain the most efficacious magnitude, frequency range and waveshape of electromagnetic fields to be used in healing bone diseases. But this matter has not been of much attention in the investigations. Modeling is an important step in understanding how bone responds to the external loadings. Much work has been done on developing models considering different properties of bone and its behavior under mechanical loadings. However, just a few researchers have included the effects of electric and magnetic loadings in their models. Methods: In this paper, the thermo-piezo-electro-magnetoelastic model of bone has been used and the effects of electric and magnetic fields with various magnitudes, types and frequencies on the existent models, have been evaluated through simulation. Result: It is shown that although the model can illustrate the effects of electric and magnetic loading magnitude alterations and various electric durations, it cannot respond appropriately to the alternating frequencies of sinusoidal waveform. Conclusion: This model should be modified to respond to alternations of electromagnetic field frequencies. © 2011 Springer-Verlag.
IFMBE Proceedings (16800737)25(4)pp. 1127-1130
In recent decades, bone fracture healing methods have been the subject of many researches. According to many clinical studies, it is proved that bone healing can be promoted by application of electric and electromagnetic field in specific amplitudes, wave shapes and frequencies. In addition to clinical experiments, many mechanical models have been proposed to simulate effects of mechanical loadings on bone healing. But only a few of these models considered the electric properties of bone. In this paper, a model of piezoelectromagnetic bone has been simulated and modified based on electromagnetoelastic theory. The effects of pulsed and sine waveforms, and different constant electric and magnetic loadings on bone are investigated. It is demonstrated that electric and magnetic loadings affect bone surface remodelling. Besides, the effects of pulse width of electric and magnetic fields on bone remodelling were evaluated. Although this model can describe the process of bone surface remodelling under electric and magnetic loadings, it cannot explain some events in bone surface remodelling such as importance of rest durations after each physiotherapeutic procedure.
Yazdchi, M.,
Yazdi M.,
Golibagh Mahyari A.,
Yazdchi, M.,
Yazdchi, M.,
Yazdchi, M.,
Yazdi M.,
Yazdi M.,
Yazdi M.,
Golibagh Mahyari A.,
Golibagh Mahyari A.,
Golibagh Mahyari A. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 346-350
Recently, it becomes significant to enhance quality of products as well as to increase quantity of products in the steel manufacturing industry. As a manufacturing gets faster, the fast and exact detection of defect is important to acquire a competitive power. Without automatic machine vision technology, steel rolling operations is not able to perform realtime inline surface defect inspection. In this paper, we propose a new defect detection algorithm based on multifractal. Then, some suitable features are extracted and presented to neural network for classification. The obtained accuracy is % 97.9. © 2009 IEEE.
Yazdchi, M.,
Golibagh Mahyari A.,
Nazeri A.,
Yazdchi, M.,
Yazdchi, M.,
Yazdchi, M.,
Golibagh Mahyari A.,
Golibagh Mahyari A.,
Golibagh Mahyari A.,
Nazeri A.,
Nazeri A. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 1071-1076
As manufacturing speed increases in the steel industry, fast and exact product inspection becomes more important. This paper deals with defect detection and classification algorithm for high-speed steel bar in coil. We enhance an acquired image by use of a special subtractive method and find the position of defect using local entropy and morphology. The extracted statistical features are then presented to a classifier. We use neural network and fuzzy inference system as a classifier and compare their results. The best accuracy, % 97.19, is obtained by the neural network. © 2008 IEEE.
Scientia Iranica (23453605)14(6)pp. 571-578
One of the most important challenges in automatic speech recognition is the case of mismatch between training and test data. Conventional methods for improving recognition robustness seek to eliminate or reduce the mismatch, e.g. enhancement of the speech by adapting the statistical models. Training the model in different situations is another example of these methods. The success with these techniques has been moderate compared to human performance. In this paper, an inspiration from human listeners created the motivation to develop and implement a new bidirectional neural network. This network is capable of modeling the phoneme sequence, using bidirectional connections in an isolated word recognition task. This network can correct the phoneme sequence obtained from the acoustic model to what is learned in the training phase. Acoustic feature vectors are enhanced, based on the inversion techniques in neural networks, by cascading the lexical and the acoustic model. Speech enhancement by this method has a remarkable effect in eliminating mismatch between the training and test data. The efficiency of the lexical model and speech enhancement was observed by a 17.3 percent increase in the phoneme recognition correction ratio. © Sharif University of Technology, December 2007.
International Journal of Advanced Robotic Systems (17298806)4(1)pp. 93-101
Multi-Agent systems have generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. One of the most important aspects of agent design in AI is the way agent acts or responds to the environment that the agent is acting upon. An effective action selection and behavioral method gives a powerful advantage in overall agent performance. We define a new method of action selection based on probability/priority models, we thereby introduce two efficient ways to determine probabilities using neuro-fuzzy systems and bidirectional neural networks and a new priority based system which maps the human knowledge to the action selection method. Furthermore, a behavior model is introduced to make the model more flexible.
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 2336-2348
One of the most important aspects of agent design in Al is the way agent acts or responds to the environment that the agent is acting upon. An effective action selection and behavioral method gives a powerful advantage in overall agent performance. We define a new method of action selection based on probability/priority models, we thereby introduce an efficient way to determine probabilities and a new priority based system which maps the human knowledge to action selec tion method. Furthermore, a behavior model is introduced to make the model more flexible. Copyright © IICAI 2005.
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI (10823409)2005pp. 11-18
Multi-Agent systems has generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. One of the most important aspects of agent design in AI is the way agent acts or responds to the environment that the agent is acting upon. An effective action selection and behavioral method gives a powerful advantage in overall agent performance. We define a new method of action selection based on probability/priority models in RoboCup Soccer Simulation League[6] and for simulated soccer agents, we thereby introduce an efficient way to determine probabilities and a new priority based system which maps the human knowledge to action selection method. Furthermore, a behavior model is introduced to make the model more flexible. © 2005 IEEE.
