1
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Wang J, Guo X. Automated detection of myocardial infarction based on an improved state refinement module for LSTM/GRU. Artif Intell Med 2024; 152:102865. [PMID: 38640703 DOI: 10.1016/j.artmed.2024.102865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 02/10/2024] [Accepted: 03/30/2024] [Indexed: 04/21/2024]
Abstract
Myocardial infarction (MI) is a common cardiovascular disease caused by the blockages of coronary arteries. The visual inspection of electrocardiogram (ECG) is the main diagnosis pattern, while it is taxing and time-consuming. Motivated from state refinement module for long short term memory (SRM-LSTM), we proposed two improved state refinement frameworks based on LSTM and gated recurrent unit (GRU) called ISRM-LSTM and ISRM-GRU. Both are capable of adaptively refining current states of sample points in ECG with a message passing mechanism than existing LSTM. To evaluate the validity, both are installed into convolutional network architecture and standard LSTM, GRU and Residual networks are employed as control groups across the Physikalisch-Technische Bundesanstalt database. Empirical results confirm noticeable performance improvements than control groups and several existing algorithms with an accuracy of 99.1%. To our knowledge, both modules are the first attempt to consider the interaction characteristics into deep network and improve interpretability exhibiting considerable potentials on lightweight devices thanks to only utilization of three channel ECGs.
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Affiliation(s)
- Jibin Wang
- Department of Network Engineering, Anhui Science and Technology University, Fengyang 233100, China; School of Mathematics, Tianjin University, Tianjin 300354, China.
| | - Xingtian Guo
- Clinical College, Anhui Medical University, Hefei 230031, China
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2
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Harnod Z, Lin C, Yang HW, Wang ZW, Huang HL, Lin TY, Huang CY, Lin LY, Young HWV, Lo MT. A transferable in-silico augmented ischemic model for virtual myocardial perfusion imaging and myocardial infarction detection. Med Image Anal 2024; 93:103087. [PMID: 38244290 DOI: 10.1016/j.media.2024.103087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 03/03/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024]
Abstract
This paper proposes an innovative approach to generate a generalized myocardial ischemia database by modeling the virtual electrophysiology of the heart and the 12-lead electrocardiography projected by the in-silico model can serve as a ready-to-use database for automatic myocardial infarction/ischemia (MI) localization and classification. Although the virtual heart can be created by an established technique combining the cell model with personalized heart geometry to observe the spatial propagation of depolarization and repolarization waves, we developed a strategy based on the clinical pathophysiology of MI to generate a heterogeneous database with a generic heart while maintaining clinical relevance and reduced computational complexity. First, the virtual heart is simplified into 11 regions that match the types and locations, which can be diagnosed by 12-lead ECG; the major arteries were divided into 3-5 segments from the upstream to the downstream based on the general anatomy. Second, the stenosis or infarction of the major or minor coronary artery branches can cause different perfusion drops and infarct sizes. We simulated the ischemic sites in different branches of the arteries by meandering the infarction location to elaborate on possible ECG representations, which alters the infraction's size and changes the transmembrane potential (TMP) of the myocytes associated with different levels of perfusion drop. A total of 8190 different case combinations of cardiac potentials with ischemia and MI were simulated, and the corresponding ECGs were generated by forward calculations. Finally, we trained and validated our in-silico database with a sparse representation classification (SRC) and tested the transferability of the model on the real-world Physikalisch Technische Bundesanstalt (PTB) database. The overall accuracies for localizing the MI region on the PTB data achieved 0.86, which is only 2% drop compared to that derived from the simulated database (0.88). In summary, we have shown a proof-of-concept for transferring an in-silico model to real-world database to compensate for insufficient data.
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Affiliation(s)
- Zeus Harnod
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Chen Lin
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Hui-Wen Yang
- Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, USA
| | - Zih-Wen Wang
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Han-Luen Huang
- Department of Cardiology, Hsinchu Cathay General Hospital, Hsinchu, Taiwan
| | - Tse-Yu Lin
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Chun-Yao Huang
- Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Lian-Yu Lin
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsu-Wen V Young
- Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan, Taiwan.
| | - Men-Tzung Lo
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan.
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3
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Di Costanzo A, Spaccarotella CAM, Esposito G, Indolfi C. An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review. J Clin Med 2024; 13:1033. [PMID: 38398346 PMCID: PMC10889404 DOI: 10.3390/jcm13041033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.
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Affiliation(s)
- Assunta Di Costanzo
- Division of Cardiology, Cardiovascular Research Center, University Magna Graecia Catanzaro, 88100 Catanzaro, Italy
| | - Carmen Anna Maria Spaccarotella
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80126 Naples, Italy; (C.A.M.S.)
| | - Giovanni Esposito
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80126 Naples, Italy; (C.A.M.S.)
| | - Ciro Indolfi
- Division of Cardiology, Cardiovascular Research Center, University Magna Graecia Catanzaro, 88100 Catanzaro, Italy
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4
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Yang W, Wang D, Fan W, Zhang G, Li C, Liu T. Automated atrial fibrillation and ventricular fibrillation recognition using a multi-angle dual-channel fusion network. Artif Intell Med 2023; 145:102680. [PMID: 37925208 DOI: 10.1016/j.artmed.2023.102680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 07/09/2023] [Accepted: 10/03/2023] [Indexed: 11/06/2023]
Abstract
Atrial fibrillation (AFIB) and ventricular fibrillation (VFIB) are two common cardiovascular diseases that cause numerous deaths worldwide. Medical staff usually adopt long-term ECGs as a tool to diagnose AFIB and VFIB. However, since ECG changes are occasionally subtle and similar, visual observation of ECG changes is challenging. To address this issue, we proposed a multi-angle dual-channel fusion network (MDF-Net) to automatically recognize AFIB and VFIB heartbeats in this work. MDF-Net can be seen as the fusion of a task-related component analysis (TRCA)-principal component analysis (PCA) network (TRPC-Net), a canonical correlation analysis (CCA)-PCA network (CPC-Net), and the linear support vector machine-weighted softmax with average (LS-WSA) method. TRPC-Net and CPC-Net are employed to extract deep task-related and correlation features, respectively, from two-lead ECGs, by which multi-angle feature-level information fusion is realized. Since the convolution kernels of the above methods can be directly extracted through TRCA, CCA and PCA technologies, their training time is faster than that of convolutional neural networks. Finally, LS-WSA is employed to fuse the above features at the decision level, by which the classification results are obtained. In distinguishing AFIB and VFIB heartbeats, the proposed method achieved accuracies of 99.39 % and 97.17 % in intra- and inter-patient experiments, respectively. In addition, this method performed well on noisy data and extremely imbalanced data, in which abnormal heatbeats are much less than normal heartbeats. Our proposed method has the potential to be used as a diagnostic tool in the clinic.
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Affiliation(s)
- Weiyi Yang
- School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Di Wang
- School of Electronics & Information Engineering, Tiangong University, Tianjin 300387, China
| | - Wei Fan
- College of Communication Engineering, Jilin University, Changchun 130012, China
| | - Gong Zhang
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
| | - Chunying Li
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
| | - Tong Liu
- School of Information and Electrical Engineering, Ludong University, Yantai 264025, China
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5
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Chaitanya MK, Sharma LD. Automated detection of myocardial infarction using binary Harry Hawks feature selection and ensemble KNN classifier. Comput Methods Biomech Biomed Engin 2023:1-17. [PMID: 37861426 DOI: 10.1080/10255842.2023.2270101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/17/2023] [Indexed: 10/21/2023]
Abstract
Myocardial infarction (MI), referred to as a heart attack, is a life-threatening condition that happens due to blood clots, typically, blood flow to a portion of the heart muscle is blocked. The cardiac muscle may become permanently damaged if there is insufficient oxygen and blood flow to the affected area. It's crucial to treat MI as soon as possible because even a small delay might have serious effects. The primary diagnostic tool to track and identify the signs of MI is the electrocardiogram (ECG). The complexity of MI signals combined with noise makes it difficult for clinicians to make a precise and prompt diagnosis. It might be laborious and time-consuming to manually analyse an enormous quantity of ECG data. Therefore, techniques for autonomously diagnosing from the ECG data are required. There have been numerous research on the topic of MI espial, but the majority of the algorithms are cognitively intensive when working with empirical data. The current study suggests a unique method for the efficient and reliable identification of MI. We employed circulant singular spectrum analysis (CSSA) for baseline wander removal, a 4-stage Savitzky-Golay (SG) filter to expunge powerline interference from the ECG signal and segmented in the preprocessing stage. Thus segmented ECG has been decomposed using CSSA, entropy based features are extracted. The best features are selected by using binary Harris hawk optimization (BHHO) and to machine learning (ML) classifiers like Naive Bayes, Decision tree, K-nearest neighbor (KNN), Support vector machine (SVM), and Ensemble subspace KNN. Our suggested method has been examined from both class as well as subject oriented perspectives. While the subject-oriented technique uses data from one patient for testing while using data from the other subjects for training, the class-wise strategy divides data as test data as well as training data regardless of subjects. We succeeded in achieving accuracy (A c % ) of 99.8, sensitivity (S e % ) of 99, and 100 specificity (Sp%) under the class-oriented approach. Similarly, for the subject wise strategy we achieved a mean A c % , Se%, and Sp% of 85.2, 83.1, and 84.5, respectively.
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Affiliation(s)
| | - Lakhan Dev Sharma
- School of Electronics Engineering, VIT-AP University, Amaravati, India
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6
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Charls D, Shahin M, Ahmed B. Domain and Patient Adversarial Multi-Task Learning for Arrhythmia Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083287 DOI: 10.1109/embc40787.2023.10340285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Manual screening of electrocardiograms (ECGs) for heart arrhythmias by clinicians is time-consuming and labor-intensive. A machine learning model for the automated diagnosis of heart arrhythmia from ECG signals can facilitate improved diagnosis, greater accessibility and earlier intervention for patients. The potential of such models is limited however by the small size of clinical datasets available for training. Methods that can be trained with multiple datasets to classify heart arrhythmia are needed to overcome this problem.In this paper, we propose using adversarial multi-task learning (AMTL) to extract domain and patient invariant features from two electrocardiogram databases. We further investigated the influence of beat segmentation location and beat normalization on domain invariance. Our proposed methods were tested on the MIT-BIH Arrhythmia and the St Petersburg INCART 12-lead Arrhythmia Databases. The domain adversarial models achieved a higher accuracy and average F1 score than their counterparts without domain adversarial learning. In particular, the patient and domain adversarial model improved the F1 scores on the two tested databases from 70% and 74% to 77% each.Clinical Relevance-This establishes that adversarial multitask learning with multiple datasets and multiple adversarial tasks can improve the F1 score of arrhythmia classification.
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7
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Yuan S, He Z, Zhao J, Yuan Z, Alhudhaif A, Alenezi F. Hypergraph and cross-attention-based unsupervised domain adaptation framework for cross-domain myocardial infarction localization. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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8
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Li Y, Wang G, Xia Z, Yang W, Sun L. A Dual-Scale Lead-Separated Transformer for ECG Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082646 DOI: 10.1109/embc40787.2023.10340556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
This work proposes a novel dual-scale lead-separated transformer for the auxiliary diagnosis of 12-lead electrocardiograms (ECGs). We added a new structure design on the basis of traditional ECG signal processing, which led to our model with only 2.6M parameters. The output of the system is the classification results. The fixed 0.5 second ECG segments of each lead are interpreted as independent patches. Together with the reduced dimension signal, patches form a dual-scale representation. As a method to reduce interference from segments with low correlation, a lead-orthogonal attention module is proposed. Experimental results show the effectiveness and scalability of our model.Clinical relevance- Our method improves the scores of clinical 12-lead ECG classification and shows generalization ability. Our model is suitable for single-label and multi-label classification tasks on clinical 12-lead ECG and is compatible with single lead classification. The integration of clinical information can further improve the effectiveness of the model.
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9
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Ramezani Moghadam S, Asl BM. Automatic diagnosis and localization of myocardial infarction using morphological features of ECG signal. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
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10
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Yang X, Ji Z. Automatic Classification Method of Arrhythmias Based on 12-Lead Electrocardiogram. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094372. [PMID: 37177575 PMCID: PMC10181542 DOI: 10.3390/s23094372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/17/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
Abstract
Cardiovascular disease is one of the main causes of death worldwide. Arrhythmias are an important group of cardiovascular diseases. The standard 12-lead electrocardiogram signals are an important tool for diagnosing arrhythmias. Although 12-lead electrocardiogram signals provide more comprehensive arrhythmia information than single-lead electrocardiogram signals, it is difficult to effectively fuse information between different leads. In addition, most of the current researches working on automatic diagnosis of cardiac arrhythmias are based on modeling and analysis of single-mode features extracted from one-dimensional electrocardiogram sequences, ignoring the frequency domain features of electrocardiogram signals. Therefore, developing an automatic arrhythmia detection algorithm based on 12-lead electrocardiogram with high accuracy and strong generalization ability is still challenging. In this paper, a multimodal feature fusion model based on the mechanism is developed. This model utilizes a dual channel deep neural network to extract different dimensional features from one-dimensional and two-dimensional electrocardiogram time-frequency maps, and combines attention mechanism to effectively fuse the important features of 12-lead, thereby obtaining richer arrhythmia information and ultimately achieving accurate classification of nine types of arrhythmia signals. This study used electrocardiogram signals from a mixed dataset to train, validate, and evaluate the model, with an average of F1 score and average accuracy reached 0.85 and 0.97, respectively. Experimental results show that our algorithm has stable and reliable performance, so it is expected to have good practical application potential.
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Affiliation(s)
- Xiao Yang
- College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Zhong Ji
- College of Bioengineering, Chongqing University, Chongqing 400030, China
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11
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Geng Q, Liu H, Gao T, Liu R, Chen C, Zhu Q, Shu M. An ECG Classification Method Based on Multi-Task Learning and CoT Attention Mechanism. Healthcare (Basel) 2023; 11:healthcare11071000. [PMID: 37046927 PMCID: PMC10094198 DOI: 10.3390/healthcare11071000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/25/2023] [Accepted: 03/26/2023] [Indexed: 04/03/2023] Open
Abstract
Electrocardiogram (ECG) is an efficient and simple method for the diagnosis of cardiovascular diseases and has been widely used in clinical practice. Because of the shortage of professional cardiologists and the popularity of electrocardiograms, accurate and efficient arrhythmia detection has become a hot research topic. In this paper, we propose a new multi-task deep neural network, which includes a shared low-level feature extraction module (i.e., SE-ResNet) and a task-specific classification module. Contextual Transformer (CoT) block is introduced in the classification module to dynamically model the local and global information of ECG feature sequence. The proposed method was evaluated on public CPSC2018 and PTB-XL datasets and achieved an average F1 score of 0.827 on the CPSC2018 dataset and an average F1 score of 0.833 on the PTB-XL dataset.
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Affiliation(s)
- Quancheng Geng
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Hui Liu
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Tianlei Gao
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Rensong Liu
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Chao Chen
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Qing Zhu
- Department of Cardiology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Minglei Shu
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
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12
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A robust myocardial infarction localization system based on multi-branch residual shrinkage network and active learning with clustering. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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13
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Sun Q, Xu Z, Liang C, Zhang F, Li J, Liu R, Chen T, Ji B, Chen Y, Wang C. A dynamic learning-based ECG feature extraction method for myocardial infarction detection. Physiol Meas 2023; 43. [PMID: 36595315 DOI: 10.1088/1361-6579/acaa1a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/08/2022] [Indexed: 12/13/2022]
Abstract
Objective.Myocardial infarction (MI) is one of the leading causes of human mortality in all cardiovascular diseases globally. Currently, the 12-lead electrocardiogram (ECG) is widely used as a first-line diagnostic tool for MI. However, visual inspection of pathological ECG variations induced by MI remains a great challenge for cardiologists, since pathological changes are usually complex and slight.Approach.To have an accuracy of the MI detection, the prominent features extracted from in-depth mining of ECG signals need to be explored. In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set.Main results.Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%.Significance.The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients.
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Affiliation(s)
- Qinghua Sun
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Zhanfei Xu
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Chunmiao Liang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Fukai Zhang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Jiali Li
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Rugang Liu
- Department of Emergency, Qilu Hospital of Shandong University, Jinan 250012, People's Republic of China
| | - Tianrui Chen
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Bing Ji
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Yuguo Chen
- Department of Emergency, Qilu Hospital of Shandong University, Jinan 250012, People's Republic of China
| | - Cong Wang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
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14
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Continuous monitoring of acute myocardial infarction with a 3-Lead ECG system. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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15
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Pan W, An Y, Guan Y, Wang J. MCA-net: A multi-task channel attention network for Myocardial infarction detection and location using 12-lead ECGs. Comput Biol Med 2022; 150:106199. [PMID: 37859291 DOI: 10.1016/j.compbiomed.2022.106199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/18/2022] [Accepted: 10/09/2022] [Indexed: 12/12/2022]
Abstract
PROBLEM Myocardial infarction (MI) is a classic cardiovascular disease (CVD) that requires prompt diagnosis. However, due to the complexity of its pathology, it is difficult for cardiologists to make an accurate diagnosis in a short period. AIM In the clinical, MI can be detected and located by the morphological changes on a 12-lead electrocardiogram (ECG). Therefore, we need to develop an automatic, high-performance, and easily scalable algorithm for MI detection and location using 12-lead ECGs to effectively reduce the burden on cardiologists. METHODS This paper proposes a multi-task channel attention network (MCA-net) for MI detection and location using 12-lead ECGs. It employs a channel attention network based on a residual structure to efficiently capture and integrate features from different leads. On top of this, a multi-task framework is used to additionally introduce the shared and complementary information between MI detection and location tasks to further enhance the model performance. RESULTS Our method is evaluated on two datasets (The PTB and PTBXL datasets). It achieved more than 90% accuracy for MI detection task on both datasets. For MI location tasks, we achieved 68.90% and 49.18% accuracy on the PTB dataset, respectively. And on the PTBXL dataset, we achieved more than 80% accuracy. CONCLUSION Numerous comparison experiments demonstrate that MCA-net outperforms the state-of-the-art methods and has a better generalization. Therefore, it can effectively assist cardiologists to detect and locate MI and has important implications for the early diagnosis of MI and patient prognosis.
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Affiliation(s)
- Weibai Pan
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China.
| | - Ying An
- School of Big Data Institute, Central South University, Changsha Hunan, China.
| | - Yuxia Guan
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China.
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China.
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16
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Uchiyama R, Okada Y, Kakizaki R, Tomioka S. End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing. Bioengineering (Basel) 2022; 9:bioengineering9090430. [PMID: 36134976 PMCID: PMC9495488 DOI: 10.3390/bioengineering9090430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/23/2022] [Accepted: 08/29/2022] [Indexed: 11/27/2022] Open
Abstract
In recent years, many studies have proposed automatic detection and localization techniques for myocardial infarction (MI) using the 12-lead electrocardiogram (ECG). Most of them applied preprocessing to the ECG signals, e.g., noise removal, trend removal, beat segmentation, and feature selection, followed by model construction and classification based on machine-learning algorithms. The selection and implementation of preprocessing methods require specialized knowledge and experience to handle ECG data. In this paper, we propose an end-to-end convolutional neural network model that detects and localizes MI without such complicated multistep preprocessing. The proposed model executes comprehensive learning for the waveform features of unpreprocessed raw ECG images captured from 12-lead ECG signals. We evaluated the classification performance of the proposed model in two experimental settings: ten-fold cross-validation where ECG images were split randomly, and two-fold cross-validation where ECG images were split into one patient and the other patients. The experimental results demonstrate that the proposed model obtained MI detection accuracies of 99.82% and 93.93% and MI localization accuracies of 99.28% and 69.27% in the first and second settings, respectively. The performance of the proposed method is higher than or comparable to that of existing state-of-the-art methods. Thus, the proposed model is expected to be an effective MI diagnosis tool that can be used in intensive care units and as wearable technology.
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Affiliation(s)
- Ryunosuke Uchiyama
- Division of Information and Electronic Engineering, Muroran Institute of Technology, 27-1, Mizumoto-cho, Muroran 050-8585, Hokkaido, Japan
| | - Yoshifumi Okada
- College of Information and Systems, Muroran Institute of Technology, 27-1, Mizumoto-cho, Muroran 050-8585, Hokkaido, Japan
- Correspondence: ; Tel.: +81-143-46-5421
| | - Ryuya Kakizaki
- Division of Information and Electronic Engineering, Muroran Institute of Technology, 27-1, Mizumoto-cho, Muroran 050-8585, Hokkaido, Japan
| | - Sekito Tomioka
- Division of Information and Electronic Engineering, Muroran Institute of Technology, 27-1, Mizumoto-cho, Muroran 050-8585, Hokkaido, Japan
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Hammad M, Alkinani MH, Gupta BB, Abd El-Latif AA. Myocardial infarction detection based on deep neural network on imbalanced data. MULTIMEDIA SYSTEMS 2022; 28:1373-1385. [DOI: 10.1007/s00530-020-00728-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 12/01/2020] [Indexed: 09/02/2023]
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18
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A novel myocardial infarction localization method using multi-branch DenseNet and spatial matching-based active semi-supervised learning. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.05.070] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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19
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Quiroz-Juárez MA, Rosales-Juárez JA, Jiménez-Ramírez O, Vázquez-Medina R, Aragón JL. ECG Patient Simulator Based on Mathematical Models. SENSORS (BASEL, SWITZERLAND) 2022; 22:5714. [PMID: 35957270 PMCID: PMC9370912 DOI: 10.3390/s22155714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
In this work, we propose a versatile, low-cost, and tunable electronic device to generate realistic electrocardiogram (ECG) waveforms, capable of simulating ECG of patients within a wide range of possibilities. A visual analysis of the clinical ECG register provides the cardiologist with vital physiological information to determine the patient's heart condition. Because of its clinical significance, there is a strong interest in algorithms and medical ECG measuring devices that acquire, preserve, and process ECG recordings with high fidelity. Bearing this in mind, the proposed electronic device is based on four different mathematical models describing macroscopic heartbeat dynamics with ordinary differential equations. Firstly, we produce full 12-lead ECG profiles by implementing a model comprising a network of heterogeneous oscillators. Then, we implement a discretized reaction-diffusion model in our electronic device to reproduce ECG waveforms from various rhythm disorders. Finally, in order to show the versatility and capabilities of our system, we include two additional models, a ring of three coupled oscillators and a model based on a quasiperiodic motion, which can reproduce a wide range of pathological conditions. With this, the proposed device can reproduce around thirty-two cardiac rhythms with the possibility of exploring different parameter values to simulate new arrhythmias with the same hardware. Our system, which is a hybrid analog-digital circuit, generates realistic ECG signals through digital-to-analog converters whose amplitudes and waveforms are controlled through an interactive and friendly graphic interface. Our ECG patient simulator arises as a promising platform for assessing the performance of electrocardiograph equipment and ECG signal processing software in clinical trials. Additionally the produced 12-lead profiles can be tested in patient monitoring systems.
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Affiliation(s)
- Mario Alan Quiroz-Juárez
- Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Queretaro 76230, Mexico;
| | - Juan Alberto Rosales-Juárez
- Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Santa Ana 1000, San Francisco Culhuacán, Mexico City 04430, Mexico; (J.A.R.-J.); (O.J.-R.)
| | - Omar Jiménez-Ramírez
- Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Santa Ana 1000, San Francisco Culhuacán, Mexico City 04430, Mexico; (J.A.R.-J.); (O.J.-R.)
| | - Rubén Vázquez-Medina
- Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Cerro Blanco 141, Colinas del Cimatario, Queretaro 76090, Mexico;
| | - José Luis Aragón
- Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Queretaro 76230, Mexico;
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Musa N, Gital AY, Aljojo N, Chiroma H, Adewole KS, Mojeed HA, Faruk N, Abdulkarim A, Emmanuel I, Folawiyo YY, Ogunmodede JA, Oloyede AA, Olawoyin LA, Sikiru IA, Katb I. A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:9677-9750. [PMID: 35821879 PMCID: PMC9261902 DOI: 10.1007/s12652-022-03868-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/26/2022] [Indexed: 06/08/2023]
Abstract
The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm. Supplementary information The online version contains supplementary material available at 10.1007/s12652-022-03868-z.
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Affiliation(s)
- Nehemiah Musa
- Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria
| | - Abdulsalam Ya’u Gital
- Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria
| | | | - Haruna Chiroma
- Computer Science and Engineering, University of Hafr Al-Batin, Hafr, Saudi Arabia
- Computer Science and Engineering , University of Hafr Al-Batin, Hafr Al-Batin, Saudi Arabia
| | - Kayode S. Adewole
- Department of Computer Science, University of Ilorin, Ilorin, Nigeria
| | - Hammed A. Mojeed
- Department of Computer Science, University of Ilorin, Ilorin, Nigeria
| | - Nasir Faruk
- Department of Physics, Sule Lamido University, Kafin Hausa, Nigeria
| | - Abubakar Abdulkarim
- Department of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, Nigeria
| | - Ifada Emmanuel
- Department of Physics, Sule Lamido University, Kafin Hausa, Nigeria
| | | | | | | | | | | | - Ibrahim Katb
- Computer Science and Engineering, University of Hafr Al-Batin, Hafr, Saudi Arabia
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Jahmunah V, Ng E, Tan RS, Oh SL, Acharya UR. Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals. Comput Biol Med 2022; 146:105550. [DOI: 10.1016/j.compbiomed.2022.105550] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/19/2022] [Accepted: 04/20/2022] [Indexed: 01/31/2023]
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22
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A Micro Neural Network for Healthcare Sensor Data Stream Classification in Sustainable and Smart Cities. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4270295. [PMID: 35785086 PMCID: PMC9249444 DOI: 10.1155/2022/4270295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022]
Abstract
A smart city is an intelligent space, in which large amounts of data are collected and analyzed using low-cost sensors and automatic algorithms. The application of artificial intelligence and Internet of Things (IoT) technologies in electronic health (E-health) can efficiently promote the development of sustainable and smart cities. The IoT sensors and intelligent algorithms enable the remote monitoring and analyzing of the healthcare data of patients, which reduces the medical and travel expenses in cities. Existing deep learning-based methods for healthcare sensor data classification have made great achievements. However, these methods take much time and storage space for model training and inference. They are difficult to be deployed in small devices to classify the physiological signal of patients in real time. To solve the above problems, this paper proposes a micro time series classification model called the micro neural network (MicroNN). The proposed model is micro enough to be deployed on tiny edge devices. MicroNN can be applied to long-term physiological signal monitoring based on edge computing devices. We conduct comprehensive experiments to evaluate the classification accuracy and computation complexity of MicroNN. Experiment results show that MicroNN performs better than the state-of-the-art methods. The accuracies on the two datasets (MIT-BIH-AR and INCART) are 98.4% and 98.1%, respectively. Finally, we present an application to show how MicroNN can improve the development of sustainable and smart cities.
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23
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Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm. ELECTRONICS 2022. [DOI: 10.3390/electronics11091495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Coronary artery disease (CAD) is a disease with high mortality and disability. By 2019, there were 197 million CAD patients in the world. Additionally, the number of disability-adjusted life years (DALYs) owing to CAD reached 182 million. It is widely known that the early and accurate diagnosis of CAD is the most efficient method to reduce the damage of CAD. In medical practice, coronary angiography is considered to be the most reliable basis for CAD diagnosis. However, unfortunately, due to the limitation of inspection equipment and expert resources, many low- and middle-income countries do not have the ability to perform coronary angiography. This has led to a large loss of life and medical burden. Therefore, many researchers expect to realize the accurate diagnosis of CAD based on conventional medical examination data with the help of machine learning and data mining technology. The goal of this study is to propose a model for early, accurate and rapid detection of CAD based on common medical test data. This model took the classical logistic regression algorithm, which is the most commonly used in medical model research as the classifier. The advantages of feature selection and feature combination of tree models were used to solve the problem of manual feature engineering in logical regression. At the same time, in order to solve the class imbalance problem in Z-Alizadeh Sani dataset, five different class balancing methods were applied to balance the dataset. In addition, according to the characteristics of the dataset, we also adopted appropriate preprocessing methods. These methods significantly improved the classification performance of logistic regression classifier in terms of accuracy, recall, precision, F1 score, specificity and AUC when used for CAD detection. The best accuracy, recall, F1 score, precision, specificity and AUC were 94.7%, 94.8%, 94.8%, 95.3%, 94.5% and 0.98, respectively. Experiments and results have confirmed that, according to common medical examination data, our proposed model can accurately identify CAD patients in the early stage of CAD. Our proposed model can be used to help clinicians make diagnostic decisions in clinical practice.
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Chen SW, Wang SL, Qi XZ, Samuri SM, Yang C. Review of ECG detection and classification based on deep learning: Coherent taxonomy, motivation, open challenges and recommendations. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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25
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Xiong P, Lee SMY, Chan G. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Front Cardiovasc Med 2022; 9:860032. [PMID: 35402563 PMCID: PMC8990170 DOI: 10.3389/fcvm.2022.860032] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/18/2022] [Indexed: 12/24/2022] Open
Abstract
Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.
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Affiliation(s)
- Ping Xiong
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Ging Chan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China
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26
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Liu S, Bin G, Wu S, Zhou Z, Bin G. Detection and Location of Myocardial Infarction from Electrocardiogram Signals Using Median Complexes and Convolutional Neural Networks. PROCEEDING OF 2021 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND APPLICATIONS 2022:1018-1030. [DOI: 10.1007/978-981-19-2456-9_102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
AbstractWhen doctors judge myocardial infarction (MI), they often introduce 12 leads as the basis for judgment. However, the repetitive labeling of nonlinear ECG signals is time-consuming and laborious. There is a need of computer-aided techniques for automatic ECG signal analysis. In this paper, we proposed a new method based on median complexes and convolutional neural networks (CNNs) for MI detection and location. Median complexes were extracted which retained the morphological features of MIs. Then, the CNN was used to determine whether each lead presented MI characteristics. Finally, the information of the 12 leads was synthesized to realize the location of MIs. Six types of MI recognition were performed, including inferior, lateral, anterolateral, anterior, and anteroseptal MIs, and non-MI. We investigated cross-database performance for MI detection and location by the proposed method, with the CNN models trained on a local database and validated by the open PTB database. Experimental results showed that the proposed method yielded F1 scores of 84.6% and 80.4% for the local and PTB test datasets, respectively. The proposed method outperformed the traditional hand-crafted method. With satisfying cross-database and generalization performance, the proposed CNN method may be used as a new method for improved MI detection and location in ECG signals.
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27
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Mohebbanaaz, Padma Sai Y, Rajani Kumari L. Cognitive assistant DeepNet model for detection of cardiac arrhythmia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103221] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Liu W, Ji J, Chang S, Wang H, He J, Huang Q. EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms. BIOSENSORS 2021; 12:15. [PMID: 35049642 PMCID: PMC8773852 DOI: 10.3390/bios12010015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/27/2021] [Accepted: 12/28/2021] [Indexed: 06/14/2023]
Abstract
Multi-branch Networks (MBNs) have been successfully applied to myocardial infarction (MI) diagnosis using 12-lead electrocardiograms. However, most existing MBNs share a fixed architecture. The absence of architecture optimization has become a significant obstacle to a more accurate diagnosis for these MBNs. In this paper, an evolving neural network named EvoMBN is proposed for MI diagnosis. It utilizes a genetic algorithm (GA) to automatically learn the optimal MBN architectures. A novel fixed-length encoding method is proposed to represent each architecture. In addition, the crossover, mutation, selection, and fitness evaluation of the GA are defined to ensure the architecture can be optimized through evolutional iterations. A novel Lead Squeeze and Excitation (LSE) block is designed to summarize features from all the branch networks. It consists of a fully-connected layer and an LSE mechanism that assigns weights to different leads. Five-fold inter-patient cross validation experiments on MI detection and localization are performed using the PTB diagnostic database. Moreover, the model architecture learned from the PTB database is transferred to the PTB-XL database without any changes. Compared with existing studies, our EvoMBN shows superior generalization and the efficiency of its flexible architecture is suitable for auxiliary MI diagnosis in real-world.
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Martin H, Morar U, Izquierdo W, Cabrerizo M, Cabrera A, Adjouadi M. Real-time frequency-independent single-Lead and single-beat myocardial infarction detection. Artif Intell Med 2021; 121:102179. [PMID: 34763801 DOI: 10.1016/j.artmed.2021.102179] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/29/2021] [Accepted: 09/21/2021] [Indexed: 11/26/2022]
Abstract
This study proposes a novel real-time frequency-independent myocardial infarction detector for Lead II electrocardiograms. The underlying Deep-LSTM network is trained using the PTB-XL database, the largest to date publicly available electrocardiography dataset, and is tested over the same and the older PTB database. By testing the model over distinct datasets, collected under different conditions and from different patients, a more realistic measure of the performance can be gauged from the deployed system. The detector is trained over 3589 myocardial infarction (MI) patients and 7115 healthy controls (HC) while it is evaluated on 1076 MIs and 1840 HCs. The proposed algorithm, achieved an accuracy of 77.12%, recall/sensitivity of 75.85%, and a specificity of 83.02% over the entire PTB database; 85.07%, 81.54%, 87.31% over the PTB-XL validation set (fold 9), and 84.17%, 78.37%, 87.55% over the PTB-XL test set (fold 10). The model also achieves stable performance metrics over the frequency range of 202 Hz to 2.8 kHz. The processing time is dependent on the sampling frequency, ranging from 130 ms at 202 Hz to 1.8 s at 2.8 kHz. Such outcome is within the time required for real-time processing (less than 300 ms for fast heartbeats), between 202 Hz and 500 Hz making the algorithm practically real-time. Therefore, the proposed MI detector could be readily deployed onto existing wearable and/or portable devices and test instruments; potentially having significant societal and clinical impact in the lives of patients at risk for myocardial infarction.
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Affiliation(s)
- Harold Martin
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
| | - Ulyana Morar
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Walter Izquierdo
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Mercedes Cabrerizo
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | | | - Malek Adjouadi
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
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Rahul J, Sharma LD, Bohat VK. Short duration Vectorcardiogram based inferior myocardial infarction detection: class and subject-oriented approach. BIOMED ENG-BIOMED TE 2021; 66:489-501. [PMID: 33939896 DOI: 10.1515/bmt-2020-0329] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/30/2021] [Indexed: 12/17/2022]
Abstract
Myocardial infarction (MI) happens when blood stops circulating to an explicit segment of the heart causing harm to the heart muscles. Vectorcardiography (VCG) is a technique of recording direction and magnitude of the signals that are produced by the heart in a 3-lead representation. In this work, we present a technique for detection of MI in the inferior portion of heart using short duration VCG signals. The raw signal was pre-processed using the median and Savitzky-Golay (SG) filter. The Stationary Wavelet Transform (SWT) was used for time-invariant decomposition of the signal followed by feature extraction. The selected features using minimum-redundancy-maximum-relevance (mRMR) based feature selection method were applied to the supervised classification methods. The efficacy of the proposed method was assessed under both class-oriented and a more real-life subject-oriented approach. An accuracy of 99.14 and 89.37% were achieved respectively. Results of the proposed technique are better than existing state-of-art methods and used VCG segment is shorter. Thus, a shorter segment and a high accuracy can be helpful in the automation of timely and reliable detection of MI. The satisfactory performance achieved in the subject-oriented approach shows reliability and applicability of the proposed technique.
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Affiliation(s)
- Jagdeep Rahul
- Department of Electronics & Communication Engineering, Rajiv Gandhi University, Itanagar, Arunachal Pradesh, India
| | - Lakhan Dev Sharma
- School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
| | - Vijay Kumar Bohat
- Department of Computer Science & Engineering, Bennett University, Greater Noida, Uttar Pradesh, India
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31
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Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4123471. [PMID: 34676061 PMCID: PMC8526260 DOI: 10.1155/2021/4123471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 08/22/2021] [Accepted: 09/24/2021] [Indexed: 11/24/2022]
Abstract
Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.
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32
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He Z, Yuan Z, An P, Zhao J, Du B. MFB-LANN: A lightweight and updatable myocardial infarction diagnosis system based on convolutional neural networks and active learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 210:106379. [PMID: 34517182 DOI: 10.1016/j.cmpb.2021.106379] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 08/24/2021] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND OBJECTIVES 12 leads electrocardiogram (ECG) are widely used to diagnose myocardial infarction (MI). Generally, the symptoms of MI can be reflected by waveforms in the heartbeat, and the contribution of different ECG leads to different types of MI is different. Therefore, it is significant to use the heartbeat waveform features and the lead relationship features for multi-category MI diagnosis. Moreover, the challenge of individual differences and lightweight algorithms also need to be further resolved and explored in the ECG automatic diagnosis system. METHODS This paper presents a lightweight MI diagnosis system named multi-feature-branch lead attention neural network (MFB-LANN) via 12 leads ECG signals. It is designed based on the characteristics of the ECG lead. Specifically, 12 independent feature branches correspond to different leads, and each branch contains different convolutional layers to extract features in the heartbeat, then a novel attention module is developed named lead attention mechanism (LAM) to assign different weights to each feature branch. Finally all the weighted feature branches are fused for classification. Furthermore, to overcome individual differences, patient-specific scheme and active learning (AL) are used to train and update the model iteratively. RESULTS Experimental results based on Physikalisch-Technische Bundesanstalt (PTB) database shows that the MFB-LANN achieved satisfactory results with accuracy of 99.63% based on 5-fold cross validation under the intra-patient scheme. The patient-specific experiment yielded an average accuracy of 96.99% compared to the state-of-the-art. By contrast, the model achieved acceptable results on the hybrid database (PTB and PTB-XL), especially achieving 94.19% accuracy after the update. Moreover, the system can complete the update process and real-time diagnosis on the ARM Cortex-A72 platform. CONCLUSIONS Experiments show that the proposed method for MI diagnosis has more obvious advantages compared to other recent methods, and it has great potential to be applied to the mobile medical field.
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Affiliation(s)
- Ziyang He
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Zhiyong Yuan
- School of Computer Science, Wuhan University, Wuhan 430072, China.
| | - Panfeng An
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Jianhui Zhao
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Bo Du
- School of Computer Science, Wuhan University, Wuhan 430072, China
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Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02696-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Al Hinai G, Jammoul S, Vajihi Z, Afilalo J. Deep learning analysis of resting electrocardiograms for the detection of myocardial dysfunction, hypertrophy, and ischaemia: a systematic review. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:416-423. [PMID: 34604757 PMCID: PMC8482047 DOI: 10.1093/ehjdh/ztab048] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/14/2021] [Indexed: 01/31/2023]
Abstract
The aim of this review was to assess the evidence for deep learning (DL) analysis of resting electrocardiograms (ECGs) to predict structural cardiac pathologies such as left ventricular (LV) systolic dysfunction, myocardial hypertrophy, and ischaemic heart disease. A systematic literature search was conducted to identify published original articles on end-to-end DL analysis of resting ECG signals for the detection of structural cardiac pathologies. Studies were excluded if the ECG was acquired by ambulatory, stress, intracardiac, or implantable devices, and if the pathology of interest was arrhythmic in nature. After duplicate reviewers screened search results, 12 articles met the inclusion criteria and were included. Three articles used DL to detect LV systolic dysfunction, achieving an area under the curve (AUC) of 0.89-0.93 and an accuracy of 98%. One study used DL to detect LV hypertrophy, achieving an AUC of 0.87 and an accuracy of 87%. Six articles used DL to detect acute myocardial infarction, achieving an AUC of 0.88-1.00 and an accuracy of 83-99.9%. Two articles used DL to detect stable ischaemic heart disease, achieving an accuracy of 95-99.9%. Deep learning models, particularly those that used convolutional neural networks, outperformed rules-based models and other machine learning models. Deep learning is a promising technique to analyse resting ECG signals for the detection of structural cardiac pathologies, which has clinical applicability for more effective screening of asymptomatic populations and expedited diagnostic work-up of symptomatic patients at risk for cardiovascular disease.
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Affiliation(s)
- Ghalib Al Hinai
- Division of Cardiology, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Canada
| | - Samer Jammoul
- Division of Cardiology, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Canada
| | - Zara Vajihi
- Department of Emergency Medicine, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, H-126, Montreal, QC H3T 1E2, Canada
| | - Jonathan Afilalo
- Division of Cardiology, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Canada,Centre for Clinical Epidemiology, Jewish General Hospital, 3755 Cote Ste Catherine Rd, H-411, Montreal, QC H3T 1E2, Canada,Corresponding author. Tel: (514) 340-7540, Fax: (514) 340-7534,
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Silva D, Leonhardt S, Antink CH. Copula-Based Data Augmentation on a Deep Learning Architecture for Cardiac Sensor Fusion. IEEE J Biomed Health Inform 2021; 25:2521-2532. [PMID: 33237869 DOI: 10.1109/jbhi.2020.3040551] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In the wake of Big Data, traditional Machine Learning techniques are now often integrated in the clinical workflow. Despite more capable, Deep Learning methods are not equally accepted given their unsatiated need for great amounts of training data and transversal use of the same architectures in fundamentally different areas with weakly-substantiated adaptations. To address the former, a cardiorespiratory signal synthesizer was designed by conditional sampling from a multimodally trained stochastic system of Gaussian copulas integrated in a Markov chain. With respect to the latter, a multi-branch convolutional neural network architecture was conceived to learn the best cardiac sensor-fusion strategy at every abstraction layer. The network was tailored to the tasks of cycle detection and classification for different cardiac modality combinations by a synthesizer-based data augmentation training framework and Bayesian hyperparameter optimization. The synthesizer yielded highly realistic signals in the time, frequency and phase domains for both healthy and pathological heart cycles as well as artifacts of different modalities. Benchmarking suggested that the network is able to surpass previous architectures and data augmentation provided a performance boost in realistic data availability scenarios. These included insufficient training data volume, as low as 150 cycles long, artifact contamination and absence of a classification data type in training.
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Near real-time single-beat myocardial infarction detection from single-lead electrocardiogram using Long Short-Term Memory Neural Network. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102683] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Xiong P, Xue Y, Zhang J, Liu M, Du H, Zhang H, Hou Z, Wang H, Liu X. Localization of myocardial infarction with multi-lead ECG based on DenseNet. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106024. [PMID: 33743488 DOI: 10.1016/j.cmpb.2021.106024] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 02/22/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Myocardial infarction (MI) is a critical acute ischemic heart disease, which can be early diagnosed by electrocardiogram (ECG). However, the most research of MI localization pay more attention on the specific changes in every ECG lead independent. In our study, the research envisages the development of a novel multi-lead MI localization approach based on the densely connected convolutional network (DenseNet). METHODS Considering the correlation of the multi-lead ECG, the method using parallel 12-lead ECG, systematically exploited the correlation of the inter-lead signals. In addition, the dense connection of DenseNet enhanced the reuse of the feature information between the inter-lead and intra-lead signals. The proposed method automatically captured the effective pathological features, which improved the identification of MI. RESULTS The experimental results based on PTB diagnostic ECG database showed that the accuracy, sensitivity and specificity of the proposed method was 99.87%, 99.84% and 99.98% for 11 types of MI localization. CONCLUSIONS The proposed method has achieved superior results compared to other localization methods, which can be introduced into the clinical practice to assist the diagnosis of MI.
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Affiliation(s)
- Peng Xiong
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Yanping Xue
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Jieshuo Zhang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Physics Science and Technology, Hebei University, Baoding 071002, China
| | - Ming Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Haiman Du
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Hong Zhang
- Affiliated Hospital of Hebei University, Baoding 071002, China
| | - Zengguang Hou
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Hongrui Wang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Xiuling Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China.
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Dai H, Hwang HG, Tseng VS. Convolutional neural network based automatic screening tool for cardiovascular diseases using different intervals of ECG signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106035. [PMID: 33770545 DOI: 10.1016/j.cmpb.2021.106035] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 02/28/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Automatic screening tools can be applied to detect cardiovascular diseases (CVDs), which are the leading cause of death worldwide. As an effective and non-invasive method, electrocardiogram (ECG) based approaches are widely used to identify CVDs. Hence, this paper proposes a deep convolutional neural network (CNN) to classify five CVDs using standard 12-lead ECG signals. METHODS The Physiobank (PTB) ECG database is used in this study. Firstly, ECG signals are segmented into different intervals (one-second, two-seconds and three-seconds), without any wave detection, and three datasets are obtained. Secondly, as an alternative to any complex preprocessing, durations of raw ECG signals have been considered as input with simple min-max normalization. Lastly, a ten-fold cross-validation method is employed for one-second ECG signals and also tested on other two datasets (two-seconds and three-seconds). RESULTS Comparing to the competing approaches, the proposed CNN acquires the highest performance, having an accuracy, sensitivity, and specificity of 99.59%, 99.04%, and 99.87%, respectively, with one-second ECG signals. The overall accuracy, sensitivity, and specificity obtained are 99.80%, 99.48%, and 99.93%, respectively, using two-seconds of signals with pre-trained proposed models. The accuracy, sensitivity, and specificity of segmented ECG tested by three-seconds signals are 99.84%, 99.52%, and 99.95%, respectively. CONCLUSION The results of this study indicate that the proposed system accomplishes high performance and keeps the characterizations in brief with flexibility at the same time, which means that it has the potential for implementation in a practical, real-time medical environment.
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Affiliation(s)
- Hao Dai
- Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan.
| | - Hsin-Ginn Hwang
- Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan
| | - Vincent S Tseng
- Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
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Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate. SENSORS 2021; 21:s21051906. [PMID: 33803265 PMCID: PMC7967244 DOI: 10.3390/s21051906] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/04/2021] [Accepted: 03/04/2021] [Indexed: 12/14/2022]
Abstract
Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.
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Zhang J, Liang D, Liu A, Gao M, Chen X, Zhang X, Chen X. MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2021; 9:1900211. [PMID: 33777544 PMCID: PMC7963211 DOI: 10.1109/jtehm.2021.3064675] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/24/2021] [Accepted: 03/04/2021] [Indexed: 11/10/2022]
Abstract
Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average [Formula: see text] score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.
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Affiliation(s)
- Jing Zhang
- Department of Electronic Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
| | - Deng Liang
- Department of Electronic Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
| | - Aiping Liu
- Department of Electronic Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
| | - Min Gao
- Department of ElectrocardiogramThe First Affiliated Hospital of USTC, Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei230001China
| | - Xiang Chen
- Department of Electronic Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
| | - Xu Zhang
- Department of Electronic Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
| | - Xun Chen
- Department of Electronic Engineering and Information ScienceUniversity of Science and Technology of ChinaHefei230027China
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Chaudhary PK, Pachori RB. Automatic diagnosis of glaucoma using two-dimensional Fourier-Bessel series expansion based empirical wavelet transform. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102237] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Rai HM, Chatterjee K, Dubey A, Srivastava P. Myocardial Infarction Detection Using Deep Learning and Ensemble Technique from ECG Signals. PROCEEDINGS OF SECOND INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATIONS, AND CYBER-SECURITY 2021:717-730. [DOI: 10.1007/978-981-16-0733-2_51] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
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Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural network. Comput Biol Med 2020; 124:103939. [DOI: 10.1016/j.compbiomed.2020.103939] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 07/26/2020] [Accepted: 07/26/2020] [Indexed: 02/07/2023]
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Ebrahimi Z, Loni M, Daneshtalab M, Gharehbaghi A. A review on deep learning methods for ECG arrhythmia classification. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.eswax.2020.100033] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Chen A, Wang F, Liu W, Chang S, Wang H, He J, Huang Q. Multi-information fusion neural networks for arrhythmia automatic detection. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105479. [PMID: 32388066 DOI: 10.1016/j.cmpb.2020.105479] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/24/2020] [Accepted: 03/26/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES . The electrocardiograms (ECGs) are widely used to diagnose a variety of arrhythmias. Generally, the abnormalities of ECG signals mainly consist of ill-shaped ECG beat morphologies and irregular intervals. The ill-shaped ECG beat morphologies represent morphological information, while the irregular intervals denote the temporal information of ECG signals. But it is difficult to utilize morphological information and temporal information simultaneously when dealing with single ECG heartbeats, because RR interval is not contained in a single short heartbeat. Therefore, to handle this problems, a novel Multi-information Fusion Convolutional Bidirectional Recurrent Neural Network (MF-CBRNN) is proposed for arrhythmia automatic detection. METHODS . The MF-CBRNN is designed with two parallel hybrid branches that can simultaneously focus on the beat-based information in the ECG beats and the segment-based information in the adjacent segments of the beats. A single ECG beat provides the morphological information. At the same time, the adjacent segment of the ECG beat enriches the temporal information, so the two branches are designed to exploit the multiple information contained in ECGs. Furthermore, a combination of convolutional neural networks (CNNs) and a bidirectional long short memory (BLSTM) in each branch is utilized to capture the information from the two inputs. And all the features extracted from the two branches are fused for information aggregation. RESULTS . To evaluate the performance of the proposed model, the ECG signals from MIT-BIH databases are used for intra-patient and inter-patient paradigms. The proposed model yields an accuracy of 99.56% and an F1-score of 96.40% under the intra-patient paradigm. And it obtains an overall accuracy of 96.77% and F1-score of 77.83% under the inter-patient paradigm. CONCLUSIONS . Compared with other studies on arrhythmia detection, our method achieves a state-of-the-art performance. It indicates that the proposed model is a promising arrhythmia detection algorithm for computer-aided diagnostic systems.
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Affiliation(s)
- Aiyun Chen
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Fei Wang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Wenhan Liu
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Sheng Chang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Hao Wang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Jin He
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Qijun Huang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
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Jafarian K, Vahdat V, Salehi S, Mobin M. Automating detection and localization of myocardial infarction using shallow and end-to-end deep neural networks. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106383] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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49
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Dense Contour-Imbalance Aware framework for Colon Gland Instance Segmentation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101988] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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50
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Swain SS, Patra D, Singh YO. Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.06.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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