101
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Comparing machine learning and regression models for mortality prediction based on the Hungarian Myocardial Infarction Registry. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.04.027] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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102
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Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F, Nahavandi S, Sarrafzadegan N, Acharya UR. Machine learning-based coronary artery disease diagnosis: A comprehensive review. Comput Biol Med 2019; 111:103346. [PMID: 31288140 DOI: 10.1016/j.compbiomed.2019.103346] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 06/26/2019] [Accepted: 06/26/2019] [Indexed: 02/02/2023]
Abstract
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia.
| | - Moloud Abdar
- Département d'informatique, Université du Québec à Montréal, Montréal, Québec, Canada
| | - Mohamad Roshanzamir
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Parham M Kebria
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Fahime Khozeimeh
- Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Nizal Sarrafzadegan
- Faculty of Medicine, SPPH, University of British Columbia, Vancouver, BC, Canada; Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Khorram Ave, Isfahan, Iran
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
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104
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Han C, Shi L. Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 175:9-23. [PMID: 31104718 DOI: 10.1016/j.cmpb.2019.03.012] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 03/10/2019] [Accepted: 03/17/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE The 12 leads electrocardiogram (ECG) is an effective tool to diagnose myocardial infarction (MI) on account of its inexpensive, noninvasive and convenient. Many methodologies have been widely adopted to detect it. However, much existing method did not integrate with diagnostic logic of clinician and practical application. The aim of the paper is to provide an automated interpretable detection method of myocardial infarction. METHODS The paper presents a novel method fusing energy entropy and morphological features for MI detection via 12 leads ECG. Specifically, ECG signals are firstly decomposed by maximal overlap discrete wavelet packet transform (MODWPT), then energy entropy is calculated from the decomposed coefficients as global features. Area, kurtosis coefficient, skewness coefficient and standard deviation extracted from QRS wave and ST-T segment of ECG beat are computed as local morphological features. Combining global features based on record and local features based on beat for single lead, all the 12 leads features are fused as the ultimate feature vector. What's more, different methods including principal component analysis (PCA), linear discriminant analysis (LDA) and locality preserving projection (LPP) are employed to reduce the computational complexity and redundant information. Meanwhile, principal component features are ranked by F-value. To evaluate the proposed method, PTB (Physikalisch-Technische Bundesanstalt) database and inter-patient paradigm are employed. RESULTS Compared with different algorithms, support vector machine (SVM) using radial basis kernel function combined with 10-fold cross validation achieves the best average performance with accuracy of 99.81%, sensitivity of 99.56%, precision of 99.74% and F1 of 99.70% based on 18 features in the intra-patient paradigm. By contrast, the accuracy is 92.69% with only 22 features for the inter-patient paradigm. CONCLUSIONS The experimental results present a superior performance compared to the state-of-the-art method. Meanwhile, above approach has the characteristic of interpretability according with diagnostic logic and strategy of clinician and specific change of ECG for MI.
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Affiliation(s)
- Chuang Han
- Industrial Technology Research Institute, Zhengzhou university, Zhengzhou City, Henan, China
| | - Li Shi
- Industrial Technology Research Institute, Zhengzhou university, Zhengzhou City, Henan, China; Department of automation, Tsinghua university, Beijing City, Beijing, China.
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105
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106
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Shi M, Zhan C, He H, Jin Y, Wu R, Sun Y, Shen B. Renyi Distribution Entropy Analysis of Short-Term Heart Rate Variability Signals and Its Application in Coronary Artery Disease Detection. Front Physiol 2019; 10:809. [PMID: 31293457 PMCID: PMC6606792 DOI: 10.3389/fphys.2019.00809] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 06/07/2019] [Indexed: 02/05/2023] Open
Abstract
Coronary artery disease (CAD) is a life-threatening condition that, unless treated at an early stage, can lead to congestive heart failure, ischemic heart disease, and myocardial infarction. Early detection of diagnostic features underlying electrocardiography signals is crucial for the identification and treatment of CAD. In the present work, we proposed novel entropy called Renyi Distribution Entropy (RdisEn) for the analysis of short-term heart rate variability (HRV) signals and the detection of CAD. Our simulation experiment with synthetic, physiological, and pathological signals demonstrated that RdisEn could distinguish effectively among different subject groups. Compared to the values of sample entropy or approximation entropy, the RdisEn value was less affected by the parameter choice, and it remained stable even in short-term HRV. We have developed a combined CAD detection scheme with RdisEn and wavelet packet decomposition (WPD): (1) Normal and CAD HRV beats obtained were divided into two equal parts. (2) Feature acquisition: RdisEn and WPD-based statistical features were calculated from one part of HRV beats, and student’s t-test was performed to select clinically significant features. (3) Classification: selected features were computed from the remaining part of HRV beats and fed into K-nearest neighbor and support vector machine, to separate CAD from normal subjects. The proposed scheme automatically detected CAD with 97.5% accuracy, 100% sensitivity and 95% specificity and performed better than most of the existing schemes.
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Affiliation(s)
- Manhong Shi
- Center for Systems Biology, Soochow University, Suzhou, China.,College of Information and Network Engineering, Anhui Science and Technology University, Fengyang, China
| | - Chaoying Zhan
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Hongxin He
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Yanwen Jin
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Rongrong Wu
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Yan Sun
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
| | - Bairong Shen
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
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107
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Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier. ALGORITHMS 2019. [DOI: 10.3390/a12060118] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals’ morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal.
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108
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A radial basis probabilistic process neural network model and corresponding classification algorithm. APPL INTELL 2019. [DOI: 10.1007/s10489-018-1369-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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109
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Xu SS, Mak MW, Cheung CC. I-Vector-Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification. IEEE J Biomed Health Inform 2019; 24:717-727. [PMID: 31150349 DOI: 10.1109/jbhi.2019.2919732] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A big challenge in automatic ECG classification is the variation in the waveforms and characteristics of ECG signals among different patients. To address this issue, this paper proposes adapting a patient-independent deep neural network (DNN) using the information in the patient-dependent identity vectors (i-vectors). The adapted networks, namely i-vector adapted patient-specific DNNs (iAP-DNNs), are tuned toward the ECG characteristics of individual patients. For each patient, his/her ECG waveforms are compressed into an i-vector using a factor analysis model. Then, this i-vector is injected into the middle hidden layer of the patient-independent DNN. Stochastic gradient descent is then applied to fine-tune the whole network to form a patient-specific classifier. As a result, the adaptation makes use of not only the raw ECG waveforms from the specific patient but also the compact representation of his/her ECG characteristics through the i-vector. Analysis on the hidden-layer activations shows that by leveraging the information in the i-vectors, the iAP-DNNs are more capable of discriminating normal heartbeats against arrhythmic heartbeats than the networks that use the patient-specific ECG only for the adaptation. Experimental results based on the MIT-BIH database suggest that the iAP-DNNs perform better than existing patient-specific classifiers in terms of various performance measures. In particular, the sensitivity and specificity of the existing methods are all under the receiver operating characteristic curves of the iAP-DNNs.
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110
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Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing. APPL INTELL 2019. [DOI: 10.1007/s10489-019-01461-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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111
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MURALIDHAR BAIRY G, HAGIWARA YUKI. EMPIRICAL MODE DECOMPOSITION-BASED PROCESSING FOR AUTOMATED DETECTION OF EPILEPSY. J MECH MED BIOL 2019. [DOI: 10.1142/s0219519419400037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy is a chronic illness of the brain characterized by recurring seizure attacks. Electroencephalogram (EEG) can record the electrical activity of the brain and is extensively used to analyze and diagnose epileptic seizures. However, the EEG signals are highly non-linear and chaotic and are difficult to analyze due to their small magnitude. Hence, empirical mode decomposition (EMD), a non-linear technique, has been widely adopted to capture the subtle changes present in the EEG signals. Hence, it is an added advantage to develop an automated computer-aided diagnostic (CAD) system to detect the different brain activities from the EEG signals using machine learning approaches. In this paper, we focus on the previous works which have used the EMD technique in the automated detection of normal or epileptic EEG signals.
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Affiliation(s)
- G. MURALIDHAR BAIRY
- Faculty Department of Biomedical Engineering, Manipal Institute of Technology, Manipal 576104, India
| | - YUKI HAGIWARA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489 Singapore
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