| 1. |
- Ghareh Baghi, Arash, et al.
(author)
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Extraction of diagnostic information from phonocardiographic signal using time-growing neural network
- 2019
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In: IFMBE Proceedings. - Singapore : Springer Verlag. ; , s. 849-853, s. 849-853
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Conference paper (peer-reviewed)abstract
- This paper presents an original method for extracting medical information from a heart sound recording, so called Phonocardiographic (PCG) signal. The extracted information is employed by a binary classifier to distinguish between stenosis and regurgitation murmurs. The method is based on using our original neural network, the Time-Growing Neural Network (TGNN), in an innovative way. Children with an obstruction on their semilunar valve are considered as the patient group (PG) against a reference group (RG) of children with a regurgitation in their atrioventricular valve. PCG signals were collected from 55 children, 25/30 from the PG/RG, who referred to the Children Medical Center of Tehran University. The study was conducted according to the guidelines of Good Clinical Practices and the Declaration of Helsinki. Informed consents were obtained for all the patients prior to the data acquisition. The accuracy and sensitivity of the method was estimated to be 85% and 80% respectively, exhibiting a very good performance to be used as a part of decision support system. Such a decision support system can improve the screening accuracy in primary healthcare centers, thanks to the innovative use of TGNN.
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| 2. |
- Ghareh Baghi, Arash, et al.
(author)
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Forth heart sound detection using backward time-growing neural network
- 2020
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In: IFMBE Proceedings. - Cham : Springer Verlag. - 9783030179700 - 9783030179717 ; , s. 341-345
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Conference paper (peer-reviewed)abstract
- This paper presents a novel method for processing heart sound signal for screening forth heart sound (S4). The proposed method is based on time growing neural network with a new scheme, which we call the Backward Time-Growing Neural Network (BTGNN). The BTGNN is trained for detecting S4 in recordings of heart sound signal. In total, 83 children patients, referred to a children University hospital, participated in the study. The collected signals are composed of the subjects with and without S4 for training and testing the method. Performance of the method is evaluated using the Leave-One-Out and the repeated random sub sampling methods. The accuracy/sensitivity of the method is estimated to be 88.3%/82.4% and the structural risk is calculated to be 18.3% using repeated random sub sampling and the A-Test methods, respectively.
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| 3. |
- Ghareh Baghi, Arash, et al.
(author)
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Structural Risk Evaluation of a Deep Neural Network and a Markov Model in Extracting Medical Information from Phonocardiography
- 2018
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In: Studies in Health Technology and Informatics. - : IOS Press. - 0926-9630 .- 1879-8365. - 9781614998792 - 9781614998808 ; 251, s. 157-160
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Journal article (peer-reviewed)abstract
- This paper presents a method for exploring structural risk of any artificial intelligence-based method in bioinformatics, the A-Test method. This method provides a way to not only quantitate the structural risk associated with a classification method, but provides a graphical representation to compare the learning capacity of different classification methods. Two different methods, Deep Time Growing Neural Network (DTGNN) and Hidden Markov Model (HMM), are selected as two classification methods for comparison. Time series of heart sound signals are employed as the case study where the classifiers are trained to learn the disease-related changes. Results showed that the DTGNN offers a superior performance both in terms of the capacity and the structural risk. The A-Test method can be especially employed in comparing the learning methods with small data size.
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| 4. |
- Gharehbaghi, Arash, et al.
(author)
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A novel method for discrimination between innocent and pathological heart murmurs
- 2015
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In: Medical Engineering and Physics. - : Elsevier. - 1350-4533 .- 1873-4030. ; 37:7, s. 674-682
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Journal article (peer-reviewed)abstract
- This paper presents a novel method for discrimination between innocent and pathological murmurs using the growing time support vector machine (GTSVM). The proposed method is tailored for characterizing innocent murmurs (IM) by putting more emphasis on the early parts of the signal as IMs are often heard in early systolic phase. Individuals with mild to severe aortic stenosis (AS) and IM are the two groups subjected to analysis, taking the normal individuals with no murmur (NM) as the control group. The AS is selected due to the similarity of its murmur to IM, particularly in mild cases. To investigate the effect of the growing time windows, the performance of the GTSVM is compared to that of a conventional support vector machine (SVM), using repeated random sub-sampling method. The mean value of the classification rate/sensitivity is found to be 88%/86% for the GTSVM and 84%/83% for the SVM. The statistical evaluations show that the GTSVM significantly improves performance of the classification as compared to the SVM.
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| 5. |
- Gharehbaghi, Arash, et al.
(author)
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A pattern recognition framework for detecting dynamic changes on cyclic time series
- 2015
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In: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 48:3, s. 696-708
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Journal article (peer-reviewed)abstract
- This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease.
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| 6. |
- Gharehbaghi, Arash, et al.
(author)
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An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network
- 2019
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In: Applied Soft Computing. - : ELSEVIER. - 1568-4946 .- 1872-9681. ; 83
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Journal article (peer-reviewed)abstract
- This paper presents a novel hybrid model for classifying time series of heart sound signal using time-growing neural network. The proposed hybrid model takes segmental behaviour of heart sound signal into account by combining two different deep learning methods, the Static and the Moving Time-Growing Neural Network, which we call STGNN and MTGNN, respectively. Flexibility of the model in learning both deterministic and stochastic segments of signal allows it to learn those complicated characteristics of heart sound signal caused by any obstruction on semilunar heart valve. The model is trained to distinguish between a patient group and a reference group. The patient group is comprised of the subjects with the semilunar heart valve abnormalities including aortic stenosis, pulmonary stenosis and bicuspid aortic valve, whereas the reference group which is composed of the individuals with the heart abnormalities other than those of the reference group or the healthy ones. The model is validated using two different databases: one comprised of 140 children with various heart conditions, and the other one constituted of 50 elderly patients with aortic stenosis. Both the datasets were collected from the referrals to the University hospitals. The overall accuracy and sensitivity of the model are estimated to be 84.2% and 82.8%, respectively. The results show that the model exhibits sufficient capability to distinguish between the patient and the reference group in such a demanding clinical application. (C) 2019 Elsevier B.V. All rights reserved.
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| 7. |
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| 8. |
- Gharehbaghi, Arash, et al.
(author)
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Detection of systolic ejection click using time growing neural network
- 2014
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In: Medical Engineering and Physics. - : Elsevier. - 1350-4533 .- 1873-4030. ; 36:4, s. 477-483
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Journal article (peer-reviewed)abstract
- In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for the MLP. The results show that the TGNN performs better than do TDNN and MLP when frequency band power is used as classifier input. The performance of TGNN is also found to exhibit better immunity to noise.
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| 9. |
- Gharehbaghi, Arash, et al.
(author)
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Distinguishing Septal Heart Defects from the Valvular Regurgitation Using Intelligent Phonocardiography
- 2020
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In: Digital Personalized Health and Medicine. - : IOS Press. - 9781643680828 - 9781643680835 ; 270, s. 178-182
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Conference paper (peer-reviewed)abstract
- This paper presents an original machine learning method for extracting diagnostic medical information from heart sound recordings. The method is proposed to be integrated with an intelligent phonocardiography in order to enhance diagnostic value of this technology. The method is tailored to diagnose children with heart septal defects, the pathological condition which can bring irreversible and sometimes fatal consequences to the children. The study includes 115 children referrals to an university hospital, consisting of 6 groups of the individuals: atrial septal defects (10), healthy children with innocent murmur (25), healthy children without any murmur (25), mitral regurgitation (15), tricuspid regurgitation (15), and ventricular septal defect (25). The method is trained to detect the atrial or ventricular septal defects versus the rest of the groups. Accuracy/sensitivity and the structural risk of the method is estimated to be 91.6%/88.4% and 9.89%, using the repeated random sub sampling and the A-Test method, respectively.
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| 10. |
- Ebrahimi, Zahra, et al.
(author)
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A Review on Deep Learning Methods for ECG Arrhythmia Classification
- 2020
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In: Expert systems with applications. - : Elsevier BV. - 0957-4174 .- 1873-6793 .- 2590-1885. ; 7
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Journal article (peer-reviewed)abstract
- Deep Learning (DL) has recently become a topic of study in different applications including healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a vital role in patient monitoring. This paper presents a comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes. This study considers various types of the DL methods such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). From the 75 studies reported within 2017 and 2018, CNN is dominantly observed as the suitable technique for feature extraction, seen in 52% of the studies. DL methods showed high accuracy in correct classification of Atrial Fibrillation (AF) (100%), Supraventricular Ectopic Beats (SVEB) (99.8%), and Ventricular Ectopic Beats (VEB) (99.7%) using the GRU/LSTM, CNN, and LSTM, respectively
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| 11. |
- Ghareh Baghi, Arash, et al.
(author)
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A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network
- 2018
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In: IEEE Transactions on Neural Networks and Learning Systems. - : Institute of Electrical and Electronics Engineers Inc.. - 2162-237X .- 2162-2388. ; 29:9, s. 4102-4115
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Journal article (peer-reviewed)abstract
- This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk.
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| 12. |
- Ghareh Baghi, Arash, et al.
(author)
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An Edge Computing Method for Extracting Pathological Information from Phonocardiogram
- 2019
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In: Studies in Health Technology and Informatics. - : IOS Press. - 0926-9630 .- 1879-8365. - 9781614999867 ; 262, s. 364-367
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Journal article (peer-reviewed)abstract
- This paper presents a structure of decision support system for pediatric cardiac disease, based on an Internet of Things (IoT) framework. The structure performs the intelligent decision making at its edge processing level, which classifies the heart sound signal, to three classes of cardiac conditions, normal, mild disease, and critical disease. Three types of the errors are introduced to evaluate the performance of the processing method, Type 1, 2 and 3, defined as the incorrect classification from the critical disease, mild, and normal, respectively. The method is validated using 140 real data patient records collected from the hospital referrals. The estimated negative errors for the Type 1, and 2, are calculated to be 0% and 4.8%, against the positive errors which are 6.3% and 13.3%, respectively. The Type 3, is calculated to be 16.7%, showing a high sensitivity of the method to be used in an IoT healthcare framework.
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| 13. |
- Ghareh Baghi, Arash, et al.
(author)
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Distinguishing Aortic Stenosis from Bicuspid Aortic Valve in Children Using Intelligent Phonocardiography
- 2021
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In: IFMBE Proceedings. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030646097 ; , s. 399-406
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Conference paper (peer-reviewed)abstract
- This paper presents a machine learning method to detect and discriminate between Aortic Stenosis (AS) and Bicuspid Aortic Valve (BAV) based on heart sound analysis. Differentiation between the two heart conditions is clinically important, but complicated if relying merely on the conventional auscultation. A novel form of the Time Growing Neural Network (TGNN) is introduced for the classification purpose. The method is applied to a dataset comprised of 87 children referrals to a university hospital, from which 50 individuals are healthy (with and without innocent murmur), and the rest are abnormal with either AS (15 individuals) or BAV (22 individuals). The baseline for comparison is a Time-Delayed Neural Network (TDNN) with the same size of the feature vector and the temporal frame. We used our original validation methods, named A-Test, which provides valuable information about structural risk and also learning capacity of any supervised classification method. A-Test is an elaborated version of K-Fold validation method, in a rather profound way. Performance of the TGNN is superior comparing to the presented TDNN, with an accuracy of 85.8% against 81.5%. This method can be integrated with our intelligent phonocardiography to serve as an enhanced assessment tool in hands of nurses or practitioners at primary healthcare centers.
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| 14. |
- Ghareh Baghi, Arash, et al.
(author)
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Method AND DEVICE FOR THE DETERMINATION OF MURMUR FREQUENCY BAND
- 2014
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Patent (pop. science, debate, etc.)abstract
- The present invention is related to a method for the determination of frequency band characteristics of a heart disease. A first set of phonocardiograms are recorded from a first set of reference healthy patients, and a second set of phonocardiograms from a second set of patients suffering of a heart disease. Spectral energies of all possible frequency bands are then calculated. These spectral energies are then compared in order to determine an optimized frequency band that gives rise to the maximal distinction between spectral energies of the phonocardiograms from first and second set of phonocardiograms.
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| 15. |
- Sepehri, Amir A., et al.
(author)
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A novel method for pediatric heart sound segmentation without using the ECG
- 2010
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In: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 99:1, s. 43-48
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Journal article (peer-reviewed)abstract
- In this paper, we propose a novel method for pediatric heart sounds segmentation by paying special attention to the physiological effects of respiration on pediatric heart sounds. The segmentation is accomplished in three steps. First, the envelope of a heart sounds signal is obtained with emphasis on the first heart sound (Si) and the second heart sound (S(2)) by using short time spectral energy and autoregressive (AR) parameters of the signal. Then, the basic heart sounds are extracted taking into account the repetitive and spectral characteristics of Si and S2 sounds by using a Multi-Layer Perceptron (MLP) neural network classifier. In the final step, by considering the diastolic and systolic intervals variations due to the effect of a child's respiration, a complete and precise heart sounds end-pointing and segmentation is achieved.
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| 16. |
- Sepehri, Amir A., et al.
(author)
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Computerized screening of children congenital heart diseases
- 2008
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In: Computerized screening of children congenital heart diseases CMPB. - United Kingdom : Elsevier BV. - 0169-2607. ; 92:2, s. 186-192
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Journal article (peer-reviewed)abstract
- In this paper, we propose a method for automated screening of congenital heart diseases in children through heart sound analysis techniques. Our method relies on categorizing the pathological murmurs based on the heart sections initiating them. We show that these pathelogical murmur categories can be identified by examining the heart sound energy over specific frequency bands, which we call, Arash-Bands. To specify the Arash-Band for a category, we evaluate the energy of the heart sound over all possible frequency bands. The Arash-Band is the frequency band that provides the lowest error in clustering the instances of that category against the normal ones. The energy content of the Arash-Bands for different categories constitue a feature vector that is suitable for classification using a neural network. In order to train, and to evaluate the performance of the proposed method, we use a training data-bank, as well as a test data-bank, collectively consisting of ninety samples (normal and abnormal). Our results show that in more than 94% of cases, our method correctly identifies children with congenital heart diseases. This percentage improves to 100%, when we use the Jack-Knife validation method over all the 90 samples.
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