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Zhou C, Li X, Feng F, Zhang J, Lyu H, Wu W, Tang X, Luo B, Li D, Xiang W, Yao D. Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination. Front Physiol 2023; 14:1247587. [PMID: 37841320 PMCID: PMC10569428 DOI: 10.3389/fphys.2023.1247587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/07/2023] [Indexed: 10/17/2023] Open
Abstract
Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement. Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method's performance is further evaluated. Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm. Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples.
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Affiliation(s)
- Chenchen Zhou
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
- Guangxi Key Laboratory of Digital Infrastructure, Guangxi Information Center, Nanning, China
| | - Xiangkui Li
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Fan Feng
- Guangxi Key Laboratory of Digital Infrastructure, Guangxi Information Center, Nanning, China
| | - Jian Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - He Lyu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
| | - Weixuan Wu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
| | - Xuezhi Tang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
| | - Bin Luo
- Sichuan Huhui Software Co., Ltd., Mianyang, China
| | - Dong Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Wei Xiang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
| | - Dengju Yao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
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Ahmed AES, Abbas Q, Daadaa Y, Qureshi I, Perumal G, Ibrahim MEA. A Residual-Dense-Based Convolutional Neural Network Architecture for Recognition of Cardiac Health Based on ECG Signals. Sensors (Basel) 2023; 23:7204. [PMID: 37631741 PMCID: PMC10458913 DOI: 10.3390/s23167204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023]
Abstract
Cardiovascular disorders are often diagnosed using an electrocardiogram (ECG). It is a painless method that mimics the cyclical contraction and relaxation of the heart's muscles. By monitoring the heart's electrical activity, an ECG can be used to identify irregular heartbeats, heart attacks, cardiac illnesses, or enlarged hearts. Numerous studies and analyses of ECG signals to identify cardiac problems have been conducted during the past few years. Although ECG heartbeat classification methods have been presented in the literature, especially for unbalanced datasets, they have not proven to be successful in recognizing some heartbeat categories with high performance. This study uses a convolutional neural network (CNN) model to combine the benefits of dense and residual blocks. The objective is to leverage the benefits of residual and dense connections to enhance information flow, gradient propagation, and feature reuse, ultimately improving the model's performance. This proposed model consists of a series of residual-dense blocks interleaved with optional pooling layers for downsampling. A linear support vector machine (LSVM) classified heartbeats into five classes. This makes it easier to learn and represent features from ECG signals. We first denoised the gathered ECG data to correct issues such as baseline drift, power line interference, and motion noise. The impacts of the class imbalance are then offset by resampling techniques that denoise ECG signals. An RD-CNN algorithm is then used to categorize the ECG data for the various cardiac illnesses using the retrieved characteristics. On two benchmarked datasets, we conducted extensive simulations and assessed several performance measures. On average, we have achieved an accuracy of 98.5%, a sensitivity of 97.6%, a specificity of 96.8%, and an area under the receiver operating curve (AUC) of 0.99. The effectiveness of our suggested method for detecting heart disease from ECG data was compared with several recently presented algorithms. The results demonstrate that our method is lightweight and practical, qualifying it for continuous monitoring applications in clinical settings for automated ECG interpretation to support cardiologists.
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Affiliation(s)
- Alaa E. S. Ahmed
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (A.E.S.A.); (Y.D.); (I.Q.); (G.P.); (M.E.A.I.)
- Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt
| | - Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (A.E.S.A.); (Y.D.); (I.Q.); (G.P.); (M.E.A.I.)
| | - Yassine Daadaa
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (A.E.S.A.); (Y.D.); (I.Q.); (G.P.); (M.E.A.I.)
| | - Imran Qureshi
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (A.E.S.A.); (Y.D.); (I.Q.); (G.P.); (M.E.A.I.)
| | - Ganeshkumar Perumal
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (A.E.S.A.); (Y.D.); (I.Q.); (G.P.); (M.E.A.I.)
| | - Mostafa E. A. Ibrahim
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (A.E.S.A.); (Y.D.); (I.Q.); (G.P.); (M.E.A.I.)
- Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha 13518, Qalubia, Egypt
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Lyu H, Li X, Zhang J, Zhou C, Tang X, Xu F, Yang Y, Huang Q, Xiang W, Li D. Automated inter-patient arrhythmia classification with dual attention neural network. Comput Methods Programs Biomed 2023; 236:107560. [PMID: 37116424 DOI: 10.1016/j.cmpb.2023.107560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND AND OBJECTIVES Arrhythmia classification based on electrocardiograms (ECG) can enhance clinical diagnostic efficiency. However, due to the significant differences in the number of different categories of heartbeats, the performance of classes with fewer samples in arrhythmia classification have not met expectations under the inter-patient paradigm. This paper aims to mitigate the adverse effects of category imbalance and improve arrhythmia classification performance. METHODS We constructed a novel dual attention hybrid network (DA-Net) for arrhythmia classification under sample imbalance, based on modified convolutional networks with channel attention (MCC-Net) and sequence-to-sequence network with global attention (Seq2Seq). The refined local features of the input heartbeat are first extracted by MCC-Net and then sent to Seq2Seq for further feature fusion. By applying local and global attention in the feature extraction and fusion parts, respectively, the method fully fuses low-level feature details and high-level context information and enhances the ability to extract discriminative features. RESULTS Based on the MIT-BIH arrhythmia database, under the inter-patient paradigm without any data augmentation methods, the proposed method achieved 99.98% accuracy (ACC) for five categories. The various performance indicators are as follows: Class N: sensitivity (SEN) = 99.96%, specificity (SPEC) = 99.93%, positive predictive value (PPV) = 99.99%; Class S: SEN = 99.67%, SPEC = 99.98%, PPV = 99.56%; Class V: SEN = 100%, SPEC = 99.99%, PPV = 99.91%; Class F: SEN = 100%, PPV = 99.98%, SPEC = 97.17%. In further experiments simulating extreme cases, the model still achieved ACC of 99.54% and 98.91% in the three-category and five-category categories when the training sample size was much smaller than the test sample. CONCLUSIONS Without any data augmentation methods, the proposed model not only alleviates the negative impact of class imbalance and achieves excellent performance in all categories but also provides a new approach for dealing with class imbalance in arrhythmia classification. Additionally, our method demonstrates potential in conditions with fewer samples.
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Affiliation(s)
- He Lyu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Xiangkui Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University. 37 Guoxue Alley, Chengdu 610041. China
| | - Jian Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University. 37 Guoxue Alley, Chengdu 610041. China
| | - Chenchen Zhou
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Xuezhi Tang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Fanxin Xu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Ye Yang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Qinzhen Huang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China.
| | - Wei Xiang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China.
| | - Dong Li
- Division of Hospital Medicine, Emory School of Medicine, 201 Dowman Dr, Atlanta, GA 30322, USA
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