Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Safa Rafieivand; Mohammad Hasan Moradi; Hosein Asl Soleimani; Zahra Momayez Sanat
Volume 17, Issue 2 , September 2023, , Pages 120-130
Abstract
Esophageal mobility disorders are a type of digestive system problem characterized by abnormal bolus movement in the esophagus. The standard diagnostic method for these kinds of disorders is High-Resolution Manometry (HRM). Despite the availability of guidelines like “Chicago” for the analysis ...
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Esophageal mobility disorders are a type of digestive system problem characterized by abnormal bolus movement in the esophagus. The standard diagnostic method for these kinds of disorders is High-Resolution Manometry (HRM). Despite the availability of guidelines like “Chicago” for the analysis of HRM results, diagnosis is still a challenging task that relies on the physician's skills or requires additional assessment modalities. Additionally, it is typical for esophageal mobility disorders to co-occur in one person, leading to a more complex situation for problem identification.The current study focuses on cases who suffering from more than one disorder simultaneously. Then the problem of disorder identification can be interpreted as a multi-label classification problem. Consequently, the fuzzy classifier architecture that was previously introduced for automatic single-disorder diagnosis by the authors is modified. The presented classifier in this paper not only learns the input space from the samples but also utilizes the co-morbidity of disorders to enhance the prediction results. The outcomes show that adding this information to the learning procedure of the base classifier enhances its performance and generates a new fuzzy classifier that overcomes other multi-label classifiers. The presented method is able to differentiate esophageal mobility disorders with an average Hamming loss of 0.18 ± 0.08 which is better than other competitor methods.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Raheleh Davoodi; Mohammad Hasan Moradi
Volume 12, Issue 1 , June 2018, , Pages 25-39
Abstract
Depression is one of the most common mental disorders in the current century where early diagnosis can result in better treatment. One of the depression diagnostic methods is the analysis of the brain electrical signals. In this paper, we are seeking for a method to distinguish among the levels of the ...
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Depression is one of the most common mental disorders in the current century where early diagnosis can result in better treatment. One of the depression diagnostic methods is the analysis of the brain electrical signals. In this paper, we are seeking for a method to distinguish among the levels of the depression. The proposed model is a deep rule-based system based on the stacked principle and focuses on the interpretability of the rules alongside high accuracy. Fuzzy systems have the proper capability in the classification of medical data with various levels of uncertainty. Moreover, in the recent years, deep learning has been taken considerable attention in the field of Artificial Intelligence. In this paper, we aim to benefit from capabilities of both fields. The proposed architecture employs a robust fuzzy clustering approach that can determine an appropriate number of clusters in each layer, unsupervised and a hierarchical stacked structure to transfer the interpretable trained rules from the previous layers with the same linguistic labels to the next layer. The interpretability is due to the presence of the input space into the consequent ones. The presence of the output of the previous layer’s rules at the input space of the next parts equals to a fuzzy system with non-linear consequent or the certainty factor in a fuzzy system with linear consequent. EEG data were preprocessed and time, frequency and nonlinear features such as recurrent plot were extracted and selected and after that were employed in the proposed system. The proposed system was compared with common classifiers like Neural Net, Support Vector Machine, Naive Bayes, Decision Tree and Linear Discriminant Analysis. Accuracy results for the test data in 30 folds (49.01% in comparison to 41.42%, 40.47%, 40.01%, 38.35% and 40.28% respectively) demonstrate the considerable performance of the proposed system.
Bioinformatics / Biomedical Informatics / Medical Informatics / Health Informatics
Amin Janghorbani; Mohammad Hasan Moradi
Volume 10, Issue 3 , October 2016, , Pages 197-209
Abstract
Babies are born under 2,500 g., defined as low birth weight (LBW) babies. They are exposed to the higher risks of mortality, congenital malformations, mental retardation, and other physical and neurological impairments. 15.5 % of births around the world are LBW. Reduction of the rate of LBW births to ...
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Babies are born under 2,500 g., defined as low birth weight (LBW) babies. They are exposed to the higher risks of mortality, congenital malformations, mental retardation, and other physical and neurological impairments. 15.5 % of births around the world are LBW. Reduction of the rate of LBW births to one-third is one of the aims of United Nations Children’s Fund program. Prognosis of LBW births can play a critical role in the reduction of these cases. Also, it helps clinicians to make timely and efficient clinical decisions to save these babies' life. In this study, a hybrid framework called fuzzy evidential network with a good ability to manage different aspects of uncertainty is a selected as the LBW prognosis model. The accuracy of prognosis and the performance of the fuzzy evidential network in the management of missing values of the clinical database were investigated and compared with well-known prognosis models of LBW. The results showed that the fuzzy evidential network has higher prognosis accuracy (84.8%) than other prognosis models. On the other hand, the fusion of naïve Bayes and the fuzzy evidential network outputs resulted in higher prognosis accuracy (85.2%). In addition, the fuzzy evidential network performance in the management of uncertainty induced by imputation method, was better than other prognosis models of this study. The performance loss of this framework as the results of the missing data increment, is less than other models.
Medical Instrumentation
Rasool Baghbani; Mohammad Hasan Moradi
Volume 10, Issue 2 , August 2016, , Pages 149-160
Abstract
In this paper a new idea is suggested for designing an appropriate bio-impedance sensor in the form of a biopsy forceps to measure the electrical properties of the tissues inside the body. First, by analytically solving the Laplace equation for wedge-shaped tissue in the mouth of the forceps, the relationship ...
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In this paper a new idea is suggested for designing an appropriate bio-impedance sensor in the form of a biopsy forceps to measure the electrical properties of the tissues inside the body. First, by analytically solving the Laplace equation for wedge-shaped tissue in the mouth of the forceps, the relationship between electric potential (results from excitation current) in different points on the tissue surface and the electrical properties of the tissue are obtained. Then, to evaluate the designed bio-impedance forceps using the finite element method and the experimental data obtained for different tissues by Gabriel et al., modeling and simulation were done and it was found that the voltages obtained for all of the tissues inside the mouth of the forceps at different frequencies from 50 Hz to 5 MHz, are consistent with that of the analytical method. To investigate the influence of the opening angle of the forceps, measurements were done at different angles and it was found that for small opening angles, measurements are more accurate. Also, electrical properties were measured by changing the size and shape of the tissue and it was found that the designed forceps is non-sensitive and robust to the changes of the volume and shape of the tissue. A prototype of the designed bio-impedance forceps was fabricated. The forceps was experimentally validated by measuring conductivity of the Phosphate Buffered Saline (PBS) solution with different concentrations at frequency range of 50KHz to 1MHz using an impedance analyzer system. To examine the accuracy of measured conductivity values, the Van Der Pauw method was implemented and electrical conductivity of the PBS was measured again. Results showed that measured conductivities by means of the bio-impedance forceps were accurate with an error less than 4%.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Masoumeh Rahimi; Mohammad Hasan Moradi; Farnaz Ghassemi
Volume 10, Issue 1 , May 2016, , Pages 59-68
Abstract
The aim of this paper is to study brain effective connectivity based on directed transform function (DTF) using granger causality method. This connectivity was calculated for recorded data in different states of attention and consciousness, forming four different classes: attention-consciousness, attention-unconsciousness, ...
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The aim of this paper is to study brain effective connectivity based on directed transform function (DTF) using granger causality method. This connectivity was calculated for recorded data in different states of attention and consciousness, forming four different classes: attention-consciousness, attention-unconsciousness, inattention-consciousness, and inattention-unconsciousness. Some common indices were extracted and calculated from the connectivity matrices. Indices of these four classes were compared to see whether there is a significant difference among them or not. The Multivariate Autoregressive (MVAR) model was used to obtain the linear causal relations between channels. Furthermore, signals were divided into four frequency bands for more accurate investigation, and the existence of significant difference was investigated with two-way repeated measures test. Results indicated that and among twelve indices could show a significant difference (p<0.05) in five states out of six possible states. The only state that no feature was able to show a meaningful difference was inattention-consciousness, and inattention-unconsciousness.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Amin Janghorbani; Mohammad Hasan Moradi; Abdollah Arasteh
Volume 7, Issue 2 , June 2013, , Pages 163-174
Abstract
Acute hypotension episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prognosis of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this ...
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Acute hypotension episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prognosis of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this diagnosis. In this study two groups of features, physiological and chaotic features, were extracted from the physiological time series to be applied for prediction of AHEs in the future 1 hour time interval. The best set of the features from the extracted features were selected using Genetic Algorithm (GA) and were classified by SVM. The prediction accuracy for physiological features was 87.5% and for chaotic features was 85%. In order to improve prediction accuracy, physiological and chaotic features were employed simultaneously in feature selection and the best combination of these features was selected by GA and classified by SVM. The best prognosis accuracy, which was achieved in this study by classification of the selected features, was 95% that was better than other previously studies on the same database.
Biomedical Image Processing / Medical Image Processing
Mohammad Hasan Moradi; Mohammad Sajad Manuchehri; Reza IraniRad
Volume 5, Issue 4 , June 2011, , Pages 313-331
Abstract
During the centuries, palpation has always been a crucial procedure in diagnosing the diseases. At first, these procedures were invasive, but nowadays numerous attempts by the name of elastographyhave been madeforreaching to noninvasive methods. Elastographys basic datais tissues relative displacement ...
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During the centuries, palpation has always been a crucial procedure in diagnosing the diseases. At first, these procedures were invasive, but nowadays numerous attempts by the name of elastographyhave been madeforreaching to noninvasive methods. Elastographys basic datais tissues relative displacement which is tracked by ultrasound waves. First in these systems in order to attain the displacements gradient, an image of tissue is taken and then it is compared to image of that same tissue after applying a small mechanical impulse into it. Mechanical strain is calculated by estimating the displacements gradient and demonstrated as an image with gray levels named elastogram (strains image) .Based on how the mechanical vibration is given, ultrasound-elastography will separate into four categories as follows: static, dynamic, shear-wave and passive elastography. In static-elastography, the force is applied manually by the clinician and therefore it depends on operators skill and cannot be considerable. In dynamic type the movement of tissue is constantly provided by an external vibrator, so in order to prevent the interference of impulses we must use a rapid imaging system that eventually will cost extra expense and unavailability. Shear-wave elastography which currently is the most common method used in elastography systems,has an external vibratorLike dynamic method, but due to momentary impulses, it skips the problem of impulse interference. In passive method, physiologic movements of body will be given to tissue as itsvibration. This technique is hypothetical yet.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Isar Nejadgholi; Mohammad Hasan Moradi; Fateme Abdol Ali
Volume 4, Issue 4 , June 2010, , Pages 279-292
Abstract
Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively little number of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, Reconstructed Phase Space ...
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Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively little number of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, Reconstructed Phase Space (RPS) theory is used to classify five heartbeat types (Normal, PVC, LBBB, RBBB and PB). In the first and second method, RPS is modeled by the Gaussian mixture model (GMM) and bins, respectively and then classified by classic Bayesian classifier. In the third method, RPS is directly used to train predictor time-delayed neural networks (TDNN) and classified based on minimum prediction error. All three methods highly outperform the results reported before for patient independent heartbeat classification. The best result is achieved using GMM-Bayes method with 92.5% accuracy for patient independent classification.
Biomedical Image Processing / Medical Image Processing
Saeed Kermani; Hamid Abrishami Moghaddam; Mohammad Hasan Moradi
Volume 2, Issue 3 , June 2008, , Pages 215-231
Abstract
This paper presents a new method for quantification analysis of left ventricular performance from the sequences of cardiac magnetic resonance imaging using the three-dimension active mesh model (3DAMM). AMM is composed of topology and geometry of L V and associated elastic material properties. The ...
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This paper presents a new method for quantification analysis of left ventricular performance from the sequences of cardiac magnetic resonance imaging using the three-dimension active mesh model (3DAMM). AMM is composed of topology and geometry of L V and associated elastic material properties. The LV deformation is estimated by fitting the model to the initial sparse displacements which is measured by a new establishing point correspondence procedure. To improve the model, a new shape-based interpolation algorithm was proposed for reconstruction of the intermediate slices. The proposed approach is capable of estimating the displacement field for every desired point of the myocardial wall. Then it leads to measure dense motion field and the local dynamic parameters such as Lagrangian strain. To evaluate the performance of the proposed algorithm, eight image sequences (six real and two synthetic sets) were used and the findings were compared with those reported by other researchers. For synthetic image sequence sets, the mean square error between the length of motion field estimated by the Algorithm and the analytical values was less than 0.5 mm. The results showed that the strain measurements of the normal cases were generally consistent with the previously published values. The results of analysis on a patient data set were also consistent with his clinical evidence. In conclusion, the results demonstrated the superiority of the novel strategy with respect to our formerly presented algorithm. Furthermore, the results are comparable to the current state-of-the-art methods.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Hasan Moradi; Bahador Makki Abadi
Volume 2, Issue 2 , June 2008, , Pages 141-154
Abstract
Hish rate classification of Electromyogram (EMG) signals for controlling of prosthetic hands is still a hot topic among the rehabilitation research titles. Specially, when the degree of freedom in artificial hands increases, the classification rate decreases dramatically. In this paper, a new five layer ...
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Hish rate classification of Electromyogram (EMG) signals for controlling of prosthetic hands is still a hot topic among the rehabilitation research titles. Specially, when the degree of freedom in artificial hands increases, the classification rate decreases dramatically. In this paper, a new five layer classifier based on Neuro-Fuzzy-Genetic structure was introduced to increase the classification accuracy of EMG signals. The proposed classifier has a self- organized structure, which adaptively creates new rules according to the input features and trains the fuzzy rule weights based on the back propagation method. Finally, the genetic algorithm (GA) was employed for the final tuning stage. In this study, six subjects were asked to perform 9 different movements and their EMG signals were caught during the tasks from the six different forearm muscles. In order to remove the noises, the signals were filtered. Then the integral absolute average (IAV), Cepstrum coefficients and Wavelet Packet Coefficients with entropy pruning were extracted from the filtered signals as features. We used principal components analysis (PCA) for dimensionality reduction (234 to 10). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces the processing time for the pattern recognition. The proposed classifier was applied on the features and the results were led to higher than 96.7% classification rate for the 9 classes of movement. To make a comparison, support vector machine (SVM) was employed (76% classification rate for 9 classes) and the results showed a drastic supremacy of the proposed method.
Biomedical Image Processing / Medical Image Processing
Ali Rafiei; Mohammad Hasan Moradi; Mohammad Reza Farzaneh
Volume 1, Issue 2 , June 2007, , Pages 83-93
Abstract
A new filter was designed and approved for speckle noise removal in sonography images. In this filter, a new idea is used by using neural network learning, fuzzy information and genetic algorithm optimization. The multi-layer perceptron neural network with binary weights is used in this filter. The neighborhood ...
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A new filter was designed and approved for speckle noise removal in sonography images. In this filter, a new idea is used by using neural network learning, fuzzy information and genetic algorithm optimization. The multi-layer perceptron neural network with binary weights is used in this filter. The neighborhood window of each pixel is used as input statistical features to estimate the noise level. Then it is fuzzificated and justified by simple fuzzy rules. The membership function width and network weights are optimized by on-line genetic algorithm. The on-line algorithm contains one individual, defined as a queen. In this algorithm, the next generation is created by using only the mutation operator. The performance of this filter was compared with the other speckle noise reduction techniques such as the median and homomorphic Wiener filters. Indeed, our proposed method is able to effectively remove speckle noises while preserving the quality of fine details in the image data better than the other methods. In this system, two classic and on-line GAs are used. The classic algorithm includes 50 strings. The results showed that both of the algorithms are the same in terms of noise reduction but the classic one is slower than the other one.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Vahid Abootalebi; Mohammad Hasan Moradi; Mohammad Ali Khalilzadeh
Volume -1, Issue 1 , June 2004, , Pages 25-45
Abstract
P300 is the most predominant cognitive component of the brain signals. In this study, the single trial event related potentials recorded from the scalp, were decomposed to their time-frequency components using discrete wavelet transform. These quantities were later analyzed as the features related to ...
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P300 is the most predominant cognitive component of the brain signals. In this study, the single trial event related potentials recorded from the scalp, were decomposed to their time-frequency components using discrete wavelet transform. These quantities were later analyzed as the features related to the cognitive activities of brain. Study on these features showed that cognitive processes of the brain of ten reflected in the feature of δ and θ bands. The aim of this study, as a primary step for "lie detection using brain signals (EEG - Polygraphy)", was to design a system for discriminating between single trials involved P300 and those without it. In the first approach, an optimal discriminant function based on 9 features was designed using "Stepwise Linear Discriminant Analysis". Detection accuracy was 75% in training data and 71% in test data. More study on this method showed that almost similar accuracy could be obtained from the features of Pz channel alone. In the second approach, the modular learning strategy - based on principal component analysis and neural networks - was used. After training the systems, the maximum classification accuracy was 76% in train data and 72% in test data.
