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Berrahou N, El Alami A, Mesbah A, El Alami R, Berrahou A. Arrhythmia detection in inter-patient ECG signals using entropy rate features and RR intervals with CNN architecture. Comput Methods Biomech Biomed Engin 2024:1-20. [PMID: 39021157 DOI: 10.1080/10255842.2024.2378105] [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: 02/05/2024] [Accepted: 06/27/2024] [Indexed: 07/20/2024]
Abstract
The classification of inter-patient ECG data for arrhythmia detection using electrocardiogram (ECG) signals presents a significant challenge. Despite the recent surge in deep learning approaches, there remains a noticeable gap in the performance of inter-patient ECG classification. In this study, we introduce an innovative approach for ECG classification in arrhythmia detection by employing a 1D convolutional neural network (CNN) to leverage both morphological and temporal characteristics of cardiac cycles. Through the utilization of 1D-CNN layers, we automatically capture the morphological attributes of ECG data, allowing us to represent the shape of the ECG waveform around the R peaks. Additionally, we incorporate four RR interval features to provide temporal context, and we explore the potential application of entropy rate as a feature extraction technique for ECG signal classification. Consequently, the classification layers benefit from the combination of both temporal and learned features, leading to the achievement of the final arrhythmia classification. We validate our approach using the MIT-BIH arrhythmia dataset, employing both intra-patient and inter-patient paradigms for model training and testing. The model's generalization ability is assessed by evaluating it on the INCART dataset. The model attains average accuracy rates of 99.13% and 99.17% for 2-fold and 5-fold cross-validation, respectively, in intra-patient classification with five classes. In inter-patient classification with three and five classes, the model achieves average accuracies of 98.73% and 97.91%, respectively. For the INCART dataset, the model achieves an average accuracy of 98.20% for three classes. The experimental outcomes demonstrate the superiority of the proposed model compared to state-of-the-art models in recognizing arrhythmias. Thus, the proposed model exhibits enhanced generalization and the potential to serve as an effective solution for recognizing arrhythmias in real-world datasets characterized by class imbalances in practical applications.
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Affiliation(s)
- Nadia Berrahou
- Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Abdelmajid El Alami
- Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | | | - Rachid El Alami
- Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
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Efe E, Yavsan E. AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5863-5880. [PMID: 38872562 DOI: 10.3934/mbe.2024259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases. However, conventional methods like electrocardiography encounter challenges such as subjective analysis and limited monitoring duration. In this work, a novel hybrid model, AttBiLFNet, was proposed for precise arrhythmia detection in ECG signals, including imbalanced class distributions. AttBiLFNet integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a convolutional neural network (CNN) and incorporates an attention mechanism using the focal loss function. This architecture is capable of autonomously extracting features by harnessing BiLSTM's bidirectional information flow, which proves advantageous in capturing long-range dependencies. The attention mechanism enhances the model's focus on pertinent segments of the input sequence, which is particularly beneficial in class imbalance classification scenarios where minority class samples tend to be overshadowed. The focal loss function effectively addresses the impact of class imbalance, thereby improving overall classification performance. The proposed AttBiLFNet model achieved 99.55% accuracy and 98.52% precision. Moreover, performance metrics such as MF1, K score, and sensitivity were calculated, and the model was compared with various methods in the literature. Empirical evidence showed that AttBiLFNet outperformed other methods in terms of both accuracy and computational efficiency. The introduced model serves as a reliable tool for the timely identification of arrhythmias.
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Affiliation(s)
- Enes Efe
- Department of Electrical and Electronics Engineering, Hitit University, Corum 19030, Turkey
| | - Emrehan Yavsan
- Department of Electronics and Automation, Tekirdag Namik Kemal University, Tekirdag 59030, Turkey
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Zubair M, Woo S, Lim S, Kim D. Deep Representation Learning With Sample Generation and Augmented Attention Module for Imbalanced ECG Classification. IEEE J Biomed Health Inform 2024; 28:2461-2472. [PMID: 37851553 DOI: 10.1109/jbhi.2023.3325540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
Developing an efficient heartbeat monitoring system has become a focal point in numerous healthcare applications. Specifically, in the last few years, heartbeat classification for arrhythmia detection has gained considerable interest from researchers. This paper presents a novel deep representation learning method for the efficient detection of arrhythmic beats. To mitigate the issues associated with the imbalanced data distribution, a novel re-sampling strategy is introduced. Unlike the existing oversampling methods, the proposed technique transforms majority-class samples into minority-class samples with a novel translation loss function. This approach assists the model in learning a more generalized representation of crucially important minority class samples. Moreover, by exploiting an auxiliary feature, an augmented attention module is designed that focuses on the most relevant and target-specific information. We adopted an inter-patient classification paradigm to evaluate the proposed method. The experimental results of this study on the MIT-BIH arrhythmia database clearly indicate that the proposed model with augmented attention mechanism and over-sampling strategy significantly learns a balanced deep representation and improves the classification performance of vital heartbeats.
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Sattar S, Mumtaz R, Qadir M, Mumtaz S, Khan MA, De Waele T, De Poorter E, Moerman I, Shahid A. Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets. SENSORS (BASEL, SWITZERLAND) 2024; 24:2484. [PMID: 38676101 PMCID: PMC11054468 DOI: 10.3390/s24082484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024]
Abstract
ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.
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Affiliation(s)
- Shoaib Sattar
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (S.S.); (M.A.K.)
| | - Rafia Mumtaz
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (S.S.); (M.A.K.)
| | - Mamoon Qadir
- Federal Government Poly Clinic Hospital, Islamabad 44000, Pakistan;
| | - Sadaf Mumtaz
- NUST School of Health Sciences (NSHS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan;
| | - Muhammad Ajmal Khan
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (S.S.); (M.A.K.)
| | - Timo De Waele
- IDLab, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (T.D.W.); (E.D.P.); (I.M.); (A.S.)
| | - Eli De Poorter
- IDLab, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (T.D.W.); (E.D.P.); (I.M.); (A.S.)
| | - Ingrid Moerman
- IDLab, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (T.D.W.); (E.D.P.); (I.M.); (A.S.)
| | - Adnan Shahid
- IDLab, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (T.D.W.); (E.D.P.); (I.M.); (A.S.)
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Islam T, Washington P. Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review. BIOSENSORS 2024; 14:183. [PMID: 38667177 PMCID: PMC11048540 DOI: 10.3390/bios14040183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/27/2024] [Accepted: 04/01/2024] [Indexed: 04/28/2024]
Abstract
The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare. We explore the key areas that researchers and engineers must consider when developing a deep learning model for biosensing: the data modality, the model architecture, and the real-world use case for the model. We also discuss key ongoing challenges and potential future directions for research in this field. We aim to provide useful insights for researchers who seek to use intelligent biosensing to advance precision healthcare.
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Sutanto H. Transforming clinical cardiology through neural networks and deep learning: A guide for clinicians. Curr Probl Cardiol 2024; 49:102454. [PMID: 38342351 DOI: 10.1016/j.cpcardiol.2024.102454] [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: 02/03/2024] [Accepted: 02/08/2024] [Indexed: 02/13/2024]
Abstract
The rapid evolution of neural networks and deep learning has revolutionized various fields, with clinical cardiology being no exception. As traditional methods in cardiology encounter limitations, the integration of advanced computational techniques offers unprecedented opportunities in diagnostics and patient care. This review explores the transformative role of neural networks and deep learning in clinical cardiology, particularly focusing on their applications in electrocardiogram (ECG) analysis, imaging technologies, and cardiac prediction models. Among others, Deep Neural Networks (DNNs) have significantly surpassed traditional approaches in accuracy and efficiency in automatic ECG diagnosis. Convolutional Neural Networks (CNNs) are successfully applied in PET/CT and PET/MR imaging, enhancing diagnostic capabilities. Furthermore, deep learning algorithms have shown potential in improving cardiac prediction models, although challenges in interpretability and clinical integration remain. The review also addresses the 'black box' nature of neural networks and the ethical considerations surrounding their use in clinical settings. Overall, this review underscores the significant impact of neural networks and deep learning in cardiology, providing insights into current applications and future directions in the field.
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Affiliation(s)
- Henry Sutanto
- Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
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Song C, Zhou Z, Yu Y, Shi M, Zhang J. An improved Bi-LSTM method based on heterogeneous features fusion and attention mechanism for ECG recognition. Comput Biol Med 2024; 169:107903. [PMID: 38171263 DOI: 10.1016/j.compbiomed.2023.107903] [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: 08/20/2023] [Revised: 12/11/2023] [Accepted: 12/23/2023] [Indexed: 01/05/2024]
Abstract
Electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. However, extracting powerful deep features from ECG signal for recognition is still a challenging problem today due to the variable abnormal rhythms and noise distribution. This work proposes a Bi-LSTM algorithm based on heterogeneous features fusion and attention mechanism (HFFAM + Bi-LSTM). Combining the empirical features and the features learned by the deep learning network, HFFAM + Bi-LSTM can comprehensively extract the temporal frequency information and spatial structure information of the ECG signal. Meanwhile, a novel attention mechanism based on improved DTW (AM-DTW) is designed to analyze and control the fusion process of features. The role of AM-DTW in HFFAM + Bi-LSTM is twofold, one is to measure the feature similarity between ECG signal sets with different labels using the improved DTW, and the other is to distinguish the features into isomorphic and heterogeneous features as well as adaptive weighting of the features. It is worth mentioning that overly similar isomorphic features are filtered out to further optimize the algorithm. Thus, HFFAM + Bi-LSTM has the advantage of strengthening the heterogeneous information in the feature subspace while accounting for the isomorphic features. The accuracy of HFFAM + Bi-LSTM reaches up to 98.1 % and 97.1 % on the simulated and real datasets, respectively. Compared to the all benchmark models, the classification accuracy of HFFAM + Bi-LSTM is 1.3 % higher than the best. The experiments also demonstrate that HFFAM + Bi-LSTM has better performance compared with existing methods, which provides a new scheme for automatic detection of ECG signal.
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Affiliation(s)
- Chaoyang Song
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Zilong Zhou
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Yue Yu
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Manman Shi
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Jingxiang Zhang
- School of Science, Jiangnan University, Wuxi, 214122, China.
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Ji L, Wei Z, Hao J, Wang C. An intelligent diagnostic method of ECG signal based on Markov transition field and a ResNet. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107784. [PMID: 37660577 DOI: 10.1016/j.cmpb.2023.107784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Heart disease seriously threatens human life and health. It has the character of abruptness and is necessary to accurately monitor and intelligently diagnose electrocardiograph signals in real-time. As part of the automation of heart monitoring, the electrocardiogram (ECG) intelligent diagnosis method based on deep learning not only meets the needs of real-time and accurate but also can abandon relevant professional knowledge, which makes it possible to be promoted in the general population. METHODS This paper presents an intelligent diagnosis method based on a ResNet. Firstly, ECG signals from MIT-BIH Database are converted into 2-dim matrices by Markov Transition Field. Secondly, the matrices are used as the input of a ResNet. Then, the ResNet is able to extract high abstract features of various diseases and realize intelligent identification of five heartbeat types, including Normal Beat, Left Bundle Branch Block Beat, Right Bundle Branch Block Beat, Premature Ventricular Contraction Beat, and Atrial Premature Contraction Beat. Eventually, the proposed model is used to identify Normal Beat and Atrial Fibrillation(AF) based on the PAF Prediction Challenge Database(the PAFPC Database) to verify its generalization ability. RESULTS The experiment result shows that the intelligent diagnosis method can reach a high F1-score of 97.7% and a high accuracy upon to 99.2% on MIT-BIH Database, which are higher than the models proposed by other researchers. Its mean sensitivity and mean specificity are 97.42% and 99.54%, respectively. Moreover, the accuracy of the generalization ability verification experiment is 94.57% on the PAFPC Database, which is also higher than the results of other studies. CONCLUSION The research results show that the method proposed in this paper still achieves higher accuracy and higher F1-score than other methods without any data preprocessing. This method has better classification performance than traditional machine learning methods and other deep learning methods. That is, the method based on Markov Transition Field and a ResNet has good application prospects. At the same time, it has been verified that the model proposed in this paper also has excellent generalization ability.
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Affiliation(s)
- Lipeng Ji
- School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China.
| | - Zhonghao Wei
- School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jian Hao
- School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Chunli Wang
- Department of Geriatrics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University of Medicine, Shanghai, China
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Mao S, Sejdic E. A Review of Recurrent Neural Network-Based Methods in Computational Physiology. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:6983-7003. [PMID: 35130174 PMCID: PMC10589904 DOI: 10.1109/tnnls.2022.3145365] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Artificial intelligence and machine learning techniques have progressed dramatically and become powerful tools required to solve complicated tasks, such as computer vision, speech recognition, and natural language processing. Since these techniques have provided promising and evident results in these fields, they emerged as valuable methods for applications in human physiology and healthcare. General physiological recordings are time-related expressions of bodily processes associated with health or morbidity. Sequence classification, anomaly detection, decision making, and future status prediction drive the learning algorithms to focus on the temporal pattern and model the nonstationary dynamics of the human body. These practical requirements give birth to the use of recurrent neural networks (RNNs), which offer a tractable solution in dealing with physiological time series and provide a way to understand complex time variations and dependencies. The primary objective of this article is to provide an overview of current applications of RNNs in the area of human physiology for automated prediction and diagnosis within different fields. Finally, we highlight some pathways of future RNN developments for human physiology.
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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] [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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Su S, Zhu Z, Wan S, Sheng F, Xiong T, Shen S, Hou Y, Liu C, Li Y, Sun X, Huang J. An ECG Signal Acquisition and Analysis System Based on Machine Learning with Model Fusion. SENSORS (BASEL, SWITZERLAND) 2023; 23:7643. [PMID: 37688099 PMCID: PMC10490810 DOI: 10.3390/s23177643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
Recently, cardiovascular disease has become the leading cause of death worldwide. Abnormal heart rate signals are an important indicator of cardiovascular disease. At present, the ECG signal acquisition instruments on the market are not portable and manual analysis is applied in data processing, which cannot address the above problems. To solve these problems, this study proposes an ECG acquisition and analysis system based on machine learning. The ECG analysis system responsible for ECG signal classification includes two parts: data preprocessing and machine learning models. Multiple types of models were built for overall classification, and model fusion was conducted. Firstly, traditional models such as logistic regression, support vector machines, and XGBoost were employed, along with feature engineering that primarily included morphological features and wavelet coefficient features. Subsequently, deep learning models, including convolutional neural networks and long short-term memory networks, were introduced and utilized for model fusion classification. The system's classification accuracy for ECG signals reached 99.13%. Future work will focus on optimizing the model and developing a more portable instrument that can be utilized in the field.
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Affiliation(s)
- Shi Su
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
- Innovative Research Laboratory of Nanjing Xi-Jing Advanced Materials Technology Ltd., Nanjing 211101, China
- SEU-FEI Nano-Pico Center, Key Lab of MEMS of Ministry of Education, Collaborative Innovation, Center for Micro/Nano Fabrication, Device and System, Southeast University, Nanjing 210096, China
| | - Zhihong Zhu
- SEU-FEI Nano-Pico Center, Key Lab of MEMS of Ministry of Education, Collaborative Innovation, Center for Micro/Nano Fabrication, Device and System, Southeast University, Nanjing 210096, China
| | - Shu Wan
- SEU-FEI Nano-Pico Center, Key Lab of MEMS of Ministry of Education, Collaborative Innovation, Center for Micro/Nano Fabrication, Device and System, Southeast University, Nanjing 210096, China
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, School of Optoelectronics Engineering, Chongqing University, Chongqing 400044, China
| | - Fangqing Sheng
- School of Economics and Management, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Tianyi Xiong
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Shanshan Shen
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Yu Hou
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Cuihong Liu
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Yijin Li
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Xiaolin Sun
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Jie Huang
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
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Ding C, Wang S, Jin X, Wang Z, Wang J. A novel transformer-based ECG dimensionality reduction stacked auto-encoders for arrhythmia beat detection. Med Phys 2023; 50:5897-5912. [PMID: 37470489 DOI: 10.1002/mp.16534] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 05/12/2023] [Accepted: 05/12/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Electrocardiogram (ECG) is a powerful tool for studying cardiac activity and diagnosing various cardiovascular diseases, including arrhythmia. While machine learning and deep learning algorithms have been applied to ECG interpretation, there is still room for improvement. For instance, the commonly used Recurrent Neural Networks (RNNs), reply on its previous state to update and is therefore ineffective for parallel computing. RNN also struggles to efficiently address the issue of long-distance reliance. PURPOSE To reduce computational complexity by dimensionality reduction of ECG signals we constructed a Stacked Auto-encoders model using Transformer for ECG-based arrhythmia detection. And overcome the challenges of long-term dependencies and limited parallelizability in traditional RNNs when applied to ECG signal processing. METHODS In this paper, a Transformer-Based ECG Dimensionality Reduction Stacked Auto-encoders model is proposed for ECG-based arrhythmia detection. The transformer is used to encode ECG signals into a feature matrix, which is then dimensionally reduced using unsupervised greedy training through the four linear layers. This resulted in a low-dimensional representation of ECG features, which are subsequently classified using support vector machines (SVM) to minimize overfitting. RESULTS The proposed method is benchmarked on the MIT-BIH Arrhythmia database. In the 10-fold cross validation of beat-based arrhythmia detection, the average accuracy, sensitivity, specificity and F1 score of the proposed method are 99.83%, 98.84%, 99.84% and 99.13%, respectively, for the record-based arrhythmia detection which refers to the approach where the training and testing sets use ECG data from independent recorded patients are 88.10%, 49.79%, 91.56% and 39.95%, respectively. CONCLUSIONS Compared to other existing ECG-based arrhythmia detection methods, our proposed approach exhibits improved detection accuracy and stronger generalization for arrhythmia beats. Additionally, the use of the record-based data division method makes our approach more suitable for clinical practice.
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Affiliation(s)
- Chun Ding
- School of Software, Yunnan University, Kunming, Yunnan, China
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China
| | - Shenglun Wang
- School of Software, Yunnan University, Kunming, Yunnan, China
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China
| | - Xiaopeng Jin
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China
| | - Zhaoze Wang
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Junsong Wang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China
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Cuevas-Chávez A, Hernández Y, Ortiz-Hernandez J, Sánchez-Jiménez E, Ochoa-Ruiz G, Pérez J, González-Serna G. A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases. Healthcare (Basel) 2023; 11:2240. [PMID: 37628438 PMCID: PMC10454027 DOI: 10.3390/healthcare11162240] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 08/27/2023] Open
Abstract
According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.
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Affiliation(s)
- Alejandra Cuevas-Chávez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Yasmín Hernández
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Javier Ortiz-Hernandez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Eduardo Sánchez-Jiménez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Gilberto Ochoa-Ruiz
- School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501, Monterrey 64849, Mexico;
| | - Joaquín Pérez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Gabriel González-Serna
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
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14
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Sun J. Automatic cardiac arrhythmias classification using CNN and attention-based RNN network. Healthc Technol Lett 2023; 10:53-61. [PMID: 37265837 PMCID: PMC10230559 DOI: 10.1049/htl2.12045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/15/2022] [Accepted: 04/05/2023] [Indexed: 06/03/2023] Open
Abstract
Cardiac disease has become a severe threat to public health according to the government report. In China, there are 0.29 billion cardiac patients and early diagnosis will greatly reduce mortality and improve life quality. Electrocardiogram (ECG) signal is a priority tool in the diagnosis of heart diseases because it is non-invasive and easily available with a simple diagnostic tool of low cost. The paper proposes an automatic classification model by combing convolutional neural network (CNN) and recurrent neural network (RNN) to distinguish different types of cardiac arrhythmias. Morphology features of the raw ECG signals are extracted by CNN blocks and fed into a bidirectional gated recurrent unit (GRU) network. Attention mechanism is used to highlight specific features of the input sequence and contribute to the performance improvement of classification. The model is evaluated with two datasets considering the class imbalance problem constructed with records from MIT-BIH arrhythmia database and China Physiological Signal Challenge 2018 database. Experimental results show that this model achieves good performance with an average F1 score of 0.9110 on public dataset and 0.9082 on subject-specific dataset, which may have potential practical applications.
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Affiliation(s)
- Jie Sun
- School of Cyber Science and EngineeringNingbo University of TechnologyNingboZhejiangChina
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15
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Kumar A, Kumar M, Mahapatra RP, Bhattacharya P, Le TTH, Verma S, Mohiuddin K. Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094353. [PMID: 37177564 PMCID: PMC10181507 DOI: 10.3390/s23094353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 05/15/2023]
Abstract
Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the health data from the patient are monitored using IoT technology. Arrhythmias may suddenly lead to death and the classification of arrhythmias is considered a complicated process. In this research, an effective classification model for the classification of heart disease is developed using flamingo optimization. Initially, the ECG signal from the heart is collected and then it is subjected to the preprocessing stage; to detect and control the electrical activity of the heart, the electrocardiogram (ECG) is used. The input signals collected using IoT nodes are collectively presented in the base station for the classification using flamingo-optimization-based deep convolutional networks, which effectively predict the disease. With the aid of communication technologies and the contribution of IoT, medical professionals can easily monitor the health condition of patients. The performance is analyzed in terms of accuracy, sensitivity, and specificity.
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Affiliation(s)
- Ashwani Kumar
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, NCR Campus, Ghaziabad 201204, India
| | - Mohit Kumar
- MIT Art, Design and Technology University, Pune 412201, India
| | - Rajendra Prasad Mahapatra
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, NCR Campus, Ghaziabad 201204, India
| | - Pronaya Bhattacharya
- Department of Computer Science and Engineering, Amity School of Engineering and Technology, Research and Innovation Cell, Amity University, Kolkata 700135, India
| | - Thi-Thu-Huong Le
- Blockchain Platform Research Center, Pusan National University, Busan 609735, Republic of Korea
| | - Sahil Verma
- Faculty of Computer Science and Engineering, Uttaranchal University University, Dehradun 248007, India
| | - Khalid Mohiuddin
- Faculty of Information Systems, King Khalid University, Abha 62529, Saudi Arabia
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16
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Islam MS, Hasan KF, Sultana S, Uddin S, Lio' P, Quinn JMW, Moni MA. HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN. Neural Netw 2023; 162:271-287. [PMID: 36921434 DOI: 10.1016/j.neunet.2023.03.004] [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: 02/07/2022] [Revised: 09/21/2022] [Accepted: 03/02/2023] [Indexed: 03/07/2023]
Abstract
Deep learning-based models have achieved significant success in detecting cardiac arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the performance of such models, we have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60%, F1 score of 98.21%, a precision of 97.66%, and recall of 99.60% using MIT-BIH generated ECG. In addition, this approach significantly reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.
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Affiliation(s)
- Md Shofiqul Islam
- Faculty of Computing, Universiti Malaysia Pahang, Gambang 26300, Kuantan, Pahang, Malaysia; IBM Centre of Excellence, Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang (UMP), Lebuhraya Tun Razak, Gambang 26300, Kuantan, Pahang, Malaysia
| | - Khondokar Fida Hasan
- School of Computer Science, Queensland University of Technology (QUT), 2 George Street, Brisbane 4000, Australia
| | - Sunjida Sultana
- Department of Computer Science and Engineering, Islamic University, Kushtia 7600, Bangladesh
| | - Shahadat Uddin
- School of Project Management, Faculty of Engineering, The University of Sydney, Sydney, Australia
| | - Pietro Lio'
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Julian M W Quinn
- Bone Research Group, The Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Mohammad Ali Moni
- Artificial Intelligence & Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland St Lucia, QLD 4072, Australia.
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17
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Xu Y, Liu L, Zhang S, Xiao W. Multilayer extreme learning machine-based unsupervised deep feature representation for heartbeat classification. Soft comput 2023. [DOI: 10.1007/s00500-023-07861-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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18
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Fki Z, Ammar B, Ayed MB. Towards Automated Optimization of Residual Convolutional Neural Networks for Electrocardiogram Classification. Cognit Comput 2023:1-11. [PMID: 36819737 PMCID: PMC9930020 DOI: 10.1007/s12559-022-10103-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 12/22/2022] [Indexed: 02/19/2023]
Abstract
The interpretation of biological data such as the ElectroCardioGram (ECG) signal gives clinical information and helps to assess the heart function. There are distinct ECG patterns associated with a specific class of arrhythmia. The convolutional neural network, inspired by findings in the study of biological vision, is currently one of the most commonly employed deep neural network algorithms for ECG processing. However, deep neural network models require many hyperparameters to tune. Selecting the optimal or the best hyperparameter for the convolutional neural network algorithm is a highly challenging task. Often, we end up tuning the model manually with different possible ranges of values until a best fit model is obtained. Automatic hyperparameters tuning using Bayesian Optimization (BO) and evolutionary algorithms can provide an effective solution to current labour-intensive manual configuration approaches. In this paper, we propose to optimize the Residual one Dimensional Convolutional Neural Network model (R-1D-CNN) at two levels. At the first level, a residual convolutional layer and one-dimensional convolutional neural layers are trained to learn patient-specific ECG features over which multilayer perceptron layers can learn to produce the final class vectors of each input. This level is manual and aims to limit the search space and select the most important hyperparameters to optimize. The second level is automatic and based on our proposed BO-based algorithm. Our optimized proposed architecture (BO-R-1D-CNN) is evaluated on two publicly available ECG datasets. Comparative experimental results demonstrate that our BO-based algorithm achieves an optimal rate of 99.95% for the MIT-BIH database to discriminate between five kinds of heartbeats, including normal heartbeats, left bundle branch block, atrial premature, right bundle branch block, and premature ventricular contraction. Moreover, experiments demonstrate that the proposed architecture fine-tuned with BO achieves a higher accuracy tested on the 10,000 ECG patients dataset compared to the other proposed architectures. Our optimized architecture achieves excellent results compared to previous works on the two benchmark datasets.
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Affiliation(s)
- Zeineb Fki
- REGIM-Lab.: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, Sfax, 3038 Tunisia
| | - Boudour Ammar
- REGIM-Lab.: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, Sfax, 3038 Tunisia
| | - Mounir Ben Ayed
- REGIM-Lab.: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, Sfax, 3038 Tunisia
- Faculty of Science of Sfax (FSS), University of Sfax, Road of Soukra km 4, Sfax, 3038 Tunisia
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19
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Boulif A, Ananou B, Ouladsine M, Delliaux S. A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques. Bioinform Biol Insights 2023; 17:11779322221149600. [PMID: 36798080 PMCID: PMC9926384 DOI: 10.1177/11779322221149600] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 12/18/2022] [Indexed: 02/12/2023] Open
Abstract
In the health care and medical domain, it has been proven challenging to diagnose correctly many diseases with complicated and interferential symptoms, including arrhythmia. However, with the evolution of artificial intelligence (AI) techniques, the diagnosis and prognosis of arrhythmia became easier for the physicians and practitioners using only an electrocardiogram (ECG) examination. This review presents a synthesis of the studies conducted in the last 12 years to predict arrhythmia's occurrence by classifying automatically different heartbeat rhythms. From a variety of research academic databases, 40 studies were selected to analyze, among which 29 of them applied deep learning methods (72.5%), 9 of them addressed the problem with machine learning methods (22.5%), and 2 of them combined both deep learning and machine learning to predict arrhythmia (5%). Indeed, the use of AI for arrhythmia diagnosis is emerging in literature, although there are some challenging issues, such as the explicability of the Deep Learning methods and the computational resources needed to achieve high performance. However, with the continuous development of cloud platforms and quantum calculation for AI, we can achieve a breakthrough in arrhythmia diagnosis.
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Affiliation(s)
- Abir Boulif
- Aix-Marseille University, CNRS, LIS, Marseille, France,Abir Boulif, Aix-Marseille University, CNRS, LIS, 13397 Marseille, France.
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20
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Method for Solving Difficulties in Rhythm Classification Caused by Few Samples and Similar Characteristics in Electrocardiograms. Bioengineering (Basel) 2023; 10:bioengineering10020196. [PMID: 36829690 PMCID: PMC9952353 DOI: 10.3390/bioengineering10020196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
A method for accurately analyzing electrocardiograms (ECGs), which are obtained from electrical signals generated by cardiac activity, is essential in heart disease diagnosis. However, rhythms are typically obtained with relatively few data samples and similar characteristics, making them difficult to classify. To solve these issues, we proposed a novel method that distinguishes a given ECG rhythm using a beat score map (BSM) image. Through the proposed method, the associations between beats and previously used features, such as the R-R interval, were considered. Rhythm classification was implemented by training a convolutional neural network model and using transfer learning with the created BSM image. As a result, the proposed method for ECG rhythms with small data samples showed significant results. It also showed good performance in differentiating atrial fibrillation (AFIB) and atrial flutter (AFL) rhythms, which are difficult to distinguish due to their similar characteristics. The performance for rhythms with a small number of samples of the proposed method is 20% better than an existing method. In addition, the performance based on the F-1 score for classifying AFIB and AFL of the proposed method is 30% better than the existing method. This study solved the previous limitations caused by small sample numbers and similar rhythms.
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21
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Xia Y, Xu Y, Chen P, Zhang J, Zhang Y. Generative adversarial network with transformer generator for boosting ECG classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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22
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Diware S, Dash S, Gebregiorgis A, Joshi RV, Strydis C, Hamdioui S, Bishnoi R. Severity-Based Hierarchical ECG Classification Using Neural Networks. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:77-91. [PMID: 37015138 DOI: 10.1109/tbcas.2023.3242683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Timely detection of cardiac arrhythmia characterized by abnormal heartbeats can help in the early diagnosis and treatment of cardiovascular diseases. Wearable healthcare devices typically use neural networks to provide the most convenient way of continuously monitoring heart activity for arrhythmia detection. However, it is challenging to achieve high accuracy and energy efficiency in these smart wearable healthcare devices. In this work, we provide architecture-level solutions to deploy neural networks for cardiac arrhythmia classification. We have created a hierarchical structure after analyzing various neural network topologies where only required network components are activated to improve energy efficiency while maintaining high accuracy. In our proposed architecture, we introduce a severity-based classification approach to directly help the users of the wearable healthcare device as well as the medical professionals. Additionally, we have employed computation-in-memory based hardware to improve energy efficiency and area consumption by leveraging in-situ data processing and scalability of emerging memory technologies such as resistive random access memory (RRAM). Simulation experiments conducted using the MIT-BIH arrhythmia dataset show that the proposed architecture provides high accuracy while consuming average energy of 0.11 μJ per heartbeat classification and 0.11 mm2 area, thereby achieving 25× improvement in average energy consumption and 12× improvement in area compared to the state-of-the-art.
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23
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Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:3269144. [PMID: 36718172 PMCID: PMC9884164 DOI: 10.1155/2023/3269144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/30/2022] [Accepted: 11/24/2022] [Indexed: 01/22/2023]
Abstract
Detecting atrial fibrillation (AF) of short single-lead electrocardiogram (ECG) with low signal-to-noise ratio (SNR) is a key of the wearable heart monitoring system. This study proposed an AF detection method based on feature fusion to identify AF rhythm (A) from other three categories of ECG recordings, that is, normal rhythm (N), other rhythm (O), and noisy (∼) ECG recordings. So, the four categories, that is, N, A, O, and ∼ were identified from the database provided by PhysioNet/CinC Challenge 2017. The proposed method first unified the 9 to 60 seconds unbalanced ECG recordings into 30 s segments by copying, cutting, and symmetry. Then, 24 artificial features including waveform features, interval features, frequency-domain features, and nonlinear feature were extracted relying on prior knowledge. Meanwhile, a 13-layer one-dimensional convolutional neural network (1-D CNN) was constructed to yield 38 abstract features. Finally, 24 artificial features and 38 abstract features were fused to yield the feature matrix. Random forest was employed to classify the ECG recordings. In this study, the mean accuracy (Acc) of the four categories reached 0.857. The F 1 of N, A, and O reached 0.837. The results exhibited the proposed method had relatively satisfactory performance for identifying AF from short single-lead ECG recordings with low SNR.
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24
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Han X, Hu Z, Wang S, Zhang Y. A Survey on Deep Learning in COVID-19 Diagnosis. J Imaging 2022; 9:jimaging9010001. [PMID: 36662099 PMCID: PMC9866755 DOI: 10.3390/jimaging9010001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/05/2022] [Accepted: 12/16/2022] [Indexed: 12/31/2022] Open
Abstract
According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.
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Affiliation(s)
- Xue Han
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Zuojin Hu
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
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25
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Hammad M, Meshoul S, Dziwiński P, Pławiak P, Elgendy IA. Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:9347. [PMID: 36502049 PMCID: PMC9736761 DOI: 10.3390/s22239347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/29/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system's effectiveness is examined by using a publicly accessible database and a comparison to the current methodologies for arrhythmia classification. The results we achieved by using our multimodel are better than those obtained by using a single model and better than most of the previous detection methods. It is worth mentioning that this model produced accurate classification results on small collection of data. Experts in this field can use this model as a guide to help them make decisions and save time.
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Affiliation(s)
- Mohamed Hammad
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
| | - Souham Meshoul
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Piotr Dziwiński
- Department of Intelligent Computer Systems, Czestochowa University of Technology, Armii Krajowej 36, 42-218 Czestochowa, Poland
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Baltycka 5, 44-100 Gliwice, Poland
| | - Ibrahim A. Elgendy
- Department of Computer Science, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
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26
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A Reparameterization Multifeature Fusion CNN for Arrhythmia Heartbeats Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7401175. [DOI: 10.1155/2022/7401175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/25/2022] [Accepted: 11/03/2022] [Indexed: 11/25/2022]
Abstract
Aiming at arrhythmia heartbeats classification, a novel multifeature fusion deep learning-based method is proposed. The stationary wavelet transforms (SWT) and RR interval features are firstly extracted. Based on the traditional one-dimensional convolutional neural network (1D-CNN), a parallel multibranch convolutional network is designed for training. The subband of SWT is input into the multiscale 1D-CNN separately. The output fused with RR interval features are fed to the fully connected layer for classification. To achieve the lightweight network while maintaining the powerful inference capability of the multibranch structure, the redundant branches of the network are removed by reparameterization. Experimental results and analysis show that it outperforms existing methods by many in arrhythmic heartbeat classification.
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27
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Intelligent Recognition Algorithm of Multiple Myocardial Infarction Based on Morphological Feature Extraction. Processes (Basel) 2022. [DOI: 10.3390/pr10112348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Myocardial infarction is a type of heart disease marked by rapid progression and high mortality. In this paper, a novel intelligent recognition algorithm of multiple myocardial infarctions using a bidirectional long short-term memory (BiLSTM) neural network classification was proposed. This algorithm was based on morphological feature extraction, which can greatly improve the diagnostic efficiency of doctors for different kinds of myocardial infarction diseases. The algorithm includes noise reduction and beat segmentation of electrocardiogram (ECG) signals from the Physikalisch-Technische Bundesanstalt (PTB) database. According to the medical diagnosis guide, the distance feature of the whole waveform and the amplitude feature of the branch lead waveform are extracted. According to the extracted features, the long short-term memory network (LSTM) and the BiLSTM neural networks are built to classify and recognize heartbeats. The experimental results show that the accuracy of the morphological feature + BiLSTM algorithm in MI detection is 99.4%. At the same time, among the six common myocardial infarction diseases, the location and recognition rate of the culprit vessel is high. The sensitivity, specificity, PPV, NPV, and F1 score parameters all reach more than 98.4%, and the kappa coefficient also reaches 0.983, while the overall accuracy reaches 98.6%. The accuracy of this algorithm is improved by at least 1% compared with that of other existing algorithms. Thus, this study exhibits a very important clinical application value.
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Aguilera-Martos I, García-Vico ÁM, Luengo J, Damas S, Melero FJ, Valle-Alonso JJ, Herrera F. TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.10.062] [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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29
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Hong J, Li HJ, Yang CC, Han CL, Hsieh JC. A clinical study on Atrial Fibrillation, Premature Ventricular Contraction, and Premature Atrial Contraction screening based on an ECG deep learning model. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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30
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Liu J, Li Z, Jin Y, Liu Y, Liu C, Zhao L, Chen X. A review of arrhythmia detection based on electrocardiogram with artificial intelligence. Expert Rev Med Devices 2022; 19:549-560. [DOI: 10.1080/17434440.2022.2115887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Jinlei Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Zhiyuan Li
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yanrui Jin
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yunqing Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Chengliang Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | - Liqun Zhao
- Department of Cardiology, Shanghai First People’s Hospital Affiliated to Shanghai Jiao Tong University, 100 Haining Road, Shanghai 200080, China
| | - Xiaojun Chen
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
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Ma C, Lan K, Wang J, Yang Z, Zhang Z. Arrhythmia detection based on multi-scale fusion of hybrid deep models from single lead ECG recordings: A multicenter dataset study. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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32
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Ramkumar M, Sarath Kumar R, Manjunathan A, Mathankumar M, Pauliah J. Auto-encoder and bidirectional long short-term memory based automated arrhythmia classification for ECG signal. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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33
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Classification of EEG Signals for Prediction of Epileptic Seizures. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Epilepsy is a common brain disorder that causes patients to face multiple seizures in a single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal epilepsy can be treated with surgery, whereas generalized epileptic seizures can be managed with medications. It has been noted that in more than 30% of cases, these medications fail to control epileptic seizures, resulting in accidents and limiting the patient’s life. Predicting epileptic seizures in such patients prior to the commencement of an oncoming seizure is critical so that the seizure can be treated with preventive medicines before it occurs. Electroencephalogram (EEG) signals of patients recorded to observe brain electrical activity during a seizure can be quite helpful in predicting seizures. Researchers have proposed methods that use machine and/or deep learning techniques to predict epileptic seizures using scalp EEG signals; however, prediction of seizures with increased accuracy is still a challenge. Therefore, we propose a three-step approach. It includes preprocessing of scalp EEG signals with PREP pipeline, which is a more sophisticated alternative to basic notch filtering. This method uses a regression-based technique to further enhance the SNR, with a combination of handcrafted, i.e., statistical features such as temporal mean, variance, and skewness, and automated features using CNN, followed by classification of interictal state and preictal state segments using LSTM to predict seizures. We train and validate our proposed technique on the CHB-MIT scalp EEG dataset and achieve accuracy of 94%, sensitivity of 93.8%, and 91.2% specificity. The proposed technique achieves better sensitivity and specificity than existing methods.
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Karri M, Annavarapu CSR, Pedapenki KK. A Real-Time Cardiac Arrhythmia Classification Using Hybrid Combination of Delta Modulation, 1D-CNN and Blended LSTM. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10949-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Classification of Electrocardiography Hybrid Convolutional Neural Network-Long Short Term Memory with Fully Connected Layer. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6348424. [PMID: 35860642 PMCID: PMC9293511 DOI: 10.1155/2022/6348424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/23/2022] [Indexed: 11/26/2022]
Abstract
Electrocardiography (ECG) is a technique for observing and recording the electrical activity of the human heart. The usage of an ECG signal is common among clinical professionals in the collection of time data for the examination of any rhythmic conditions associated with a subject. The investigation was carried out in order to computerize the assignment by exhibiting the issue using encoder-decoder techniques, creating the information that was simply typical of it, and utilising misfortune appropriation to anticipate standard or anomalous information. On a broad variety of applications such as voice recognition and prediction, the long short-term memory (LSTM) fully connected layer (FCL) and the two convolutional neural networks (CNNs) have shown superior performance over deep learning networks (DLNs). DNNs are suitable for making high points for a more divisible region and CNNs are suitable for reducing recurrence types, LSTMs are appropriate for temporary displays, in the same way as CNNs are appropriate for reducing recurrence types. The CNN, LSTM, and DNN algorithms are acceptable for viewing. The complementarity of DNNs, CNNs, and LSTMs was investigated in this research by bringing them all together under the single architectural company. The researchers got the ECG data from the MIT-BIH arrhythmia database as a result of the investigation. Our results demonstrate that the approach proposed may expressively describe ECG series and identify abnormalities via scores that outperform existing supervised and unsupervised methods in both the short term and long term. The LSTM network and FCL additionally demonstrated that the unbalanced datasets associated with the ECG beat detection problem could be consistently resolved and that they were not susceptible to the accuracy of ECG signals. It is recommended that cardiologists employ the unique technique to aid them in performing reliable and impartial interpretation of ECG data in telemedicine settings.
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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: 6] [Impact Index Per Article: 3.0] [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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Regional Collaborative Forecast of Cargo Throughput in China’s Circum-Bohai-Sea Region Based on LSTM Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5044926. [PMID: 35845869 PMCID: PMC9283028 DOI: 10.1155/2022/5044926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 05/22/2022] [Accepted: 06/13/2022] [Indexed: 11/23/2022]
Abstract
Any developed port plays a dominant role both in domestic and international trade reflecting economic prosperity of the port and nearby regions in terms of its cargo throughput and port construction. An attempt is made in this study to use long-and short-term memory (LSTM) artificial neural network method to construct the port cargo throughput prediction model. Three ports namely, Tianjin Port, Dalian Port, and Tangshan Port from China's Bohai Rim region are selected as research objects. The historical cargo throughput of each port for nearly ten years was used as the input index data for joint prediction. The cargo throughput of Bohai Port provides another way to improve the accuracy of port cargo throughput prediction. The prediction results show that the LSTM model can effectively predict the port cargo throughput; the cargo throughput forecasts between the three Bohai Rim ports have both an interactive relationship and differences.
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38
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Tao Y, Li Z, Gu C, Jiang B, Zhang Y. ECG-based expert-knowledge attention network to tachyarrhythmia recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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39
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DE-PNN: Differential Evolution-Based Feature Optimization with Probabilistic Neural Network for Imbalanced Arrhythmia Classification. SENSORS 2022; 22:s22124450. [PMID: 35746232 PMCID: PMC9227752 DOI: 10.3390/s22124450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022]
Abstract
In this research, a heartbeat classification method is presented based on evolutionary feature optimization using differential evolution (DE) and classification using a probabilistic neural network (PNN) to discriminate between normal and arrhythmic heartbeats. The proposed method follows four steps: (1) preprocessing, (2) heartbeat segmentation, (3) DE feature optimization, and (4) PNN classification. In this method, we have employed direct signal amplitude points constituting the heartbeat acquired from the ECG holter device with no secondary feature extraction step usually used in case of hand-crafted, frequency transformation or other features. The heartbeat types include normal, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature, ventricular escape, ventricular flutter and paced beat. Using ECG records from the MIT-BIH, heartbeats are identified to start at 250 ms before and end at 450 ms after the respective R-peak positions. In the next step, the DE method is applied to reduce and optimize the direct heartbeat features. Although complex and highly computational ECG heartbeat classification algorithms have been proposed in the literature, they failed to achieve high performance in detecting some minority heartbeat categories, especially for imbalanced datasets. To overcome this challenge, we propose an optimization step for the deep CNN model using a novel classification metric called the Matthews correlation coefficient (MCC). This function focuses on arrhythmia (minority) heartbeat classes by increasing their importance. Maximum MCC is used as a fitness function to identify the optimum combination of features for the uncorrelated and non-uniformly distributed eight beat class samples. The proposed DE-PNN scheme can provide better classification accuracy considering 8 classes with only 36 features optimized from a 253 element feature set implying an 85.77% reduction in direct amplitude features. Our proposed method achieved overall 99.33% accuracy, 94.56% F1, 93.84% sensitivity, and 99.21% specificity.
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40
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Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks. SENSORS 2022; 22:s22114075. [PMID: 35684694 PMCID: PMC9185309 DOI: 10.3390/s22114075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022]
Abstract
Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to their vital role in the diagnosis of cardiac abnormalities. Despite this interest, deep feature representation for ECG is still challenging and intriguing due to the inter-patient variability of the ECG’s morphological characteristics. The aim of this study was to learn a balanced deep feature representation that incorporates both the short-term and long-term morphological characteristics of ECG beats. For efficient feature extraction, we designed a temporal transition module that uses convolutional layers with different kernel sizes to capture a wide range of morphological patterns. Imbalanced data are a key issue in developing an efficient and generalized model for arrhythmia detection as they cause over-fitting to minority class samples (abnormal beats) of primary interest. To mitigate the imbalanced data issue, we proposed a novel, cost-sensitive loss function that ensures a balanced deep representation of class samples by assigning effective weights to each class. The cost-sensitive loss function dynamically alters class weights for every batch based on class distribution and model performance. The proposed method acquired an overall accuracy of 99.81% for intra-patient classification and 96.36% for the inter-patient classification of heartbeats. The experimental results reveal that the proposed approach learned a balanced representation of ECG beats by mitigating the issue of imbalanced data and achieved an improved classification performance as compared to other studies.
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41
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Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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42
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Ye X, Huang Y, Lu Q. Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model. Front Physiol 2022; 13:840011. [PMID: 35492618 PMCID: PMC9049587 DOI: 10.3389/fphys.2022.840011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
There is an increasing demand for automatic classification of standard 12-lead electrocardiogram signals in the medical field. Considering that different channels and temporal segments of a feature map extracted from the 12-lead electrocardiogram record contribute differently to cardiac arrhythmia detection, and to the classification performance, we propose a 12-lead electrocardiogram signal automatic classification model based on model fusion (CBi-DF-XGBoost) to focus on representative features along both the spatial and temporal axes. The algorithm extracts local features through a convolutional neural network and then extracts temporal features through bi-directional long short-term memory. Finally, eXtreme Gradient Boosting (XGBoost) is used to fuse the 12-lead models and domain-specific features to obtain the classification results. The 5-fold cross-validation results show that in classifying nine categories of electrocardiogram signals, the macro-average accuracy of the fusion model is 0.968, the macro-average recall rate is 0.814, the macro-average precision is 0.857, the macro-average F1 score is 0.825, and the micro-average area under the curve is 0.919. Similar experiments with some common network structures and other advanced electrocardiogram classification algorithms show that the proposed model performs favourably against other counterparts in F1 score. We also conducted ablation studies to verify the effect of the complementary information from the 12 leads and the auxiliary information of domain-specific features on the classification performance of the model. We demonstrated the feasibility and effectiveness of the XGBoost-based fusion model to classify 12-lead electrocardiogram records into nine common heart rhythms. These findings may have clinical importance for the early diagnosis of arrhythmia and incite further research. In addition, the proposed multichannel feature fusion algorithm can be applied to other similar physiological signal analyses and processing.
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Affiliation(s)
- Xiaohong Ye
- Chengyi University College, Jimei University, Xiamen, China
| | - Yuanqi Huang
- School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, China
| | - Qiang Lu
- School of Science, Jimei University, Xiamen, China
- *Correspondence: Qiang Lu,
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Zhang X, Li J, Cai Z, Zhao L, Liu C. Premature Beats Rejection Strategy on Paroxysmal Atrial Fibrillation Detection. Front Physiol 2022; 13:890139. [PMID: 35431981 PMCID: PMC9012152 DOI: 10.3389/fphys.2022.890139] [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: 03/05/2022] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
Paroxysmal atrial fibrillation (PAF) may related to the risk of thromboembolism and is the most common cardiac risk factor of cryptogenic stroke (CS). Due to its paroxysmal characteristics, it is usually diagnosed by continuous long-term ECG. Patients with paroxysmal atrial fibrillation usually have premature beats at the same time which is easy to be confused with the rhythm of atrial fibrillation. Therefore, in this article, we designed a screening algorithm for single premature beat, multi premature beats, bigeminy and trigeminy premature beats, according to their rhythm characteristics to reduce false detection caused by premature beats during the PAF detection process. The proposed elimination method was verified on ECG segments with different types of premature beats, and tested on long-term ECG data of PAF patients. ECG segments of different kinds of premature beats were selected from MIT Atrial Fibrillation database (MIT-AFDB), MIT-BIH Arrhythmia database (MIT-AR) and wearable ECG data from the China Physiological Signal Challenge 2021 (CPSC 2021). The proposed method can effectively eliminate single premature beat segments with 99.5% accuracy, and it also can eliminate more than 95% of ECG segments with other types of premature beats. We designed PAF-score as a new index to evaluate the accuracy of detection, and we also calculate the misjudged and missed segments to comprehensively evaluate the PAF detection algorithm. The proposed method get a PAF-score of 0.912 on MIT-AFDB. The proposed method also has the potential to implant low computing power wearable devices for real-time analysis.
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Affiliation(s)
| | - Jianqing Li
- *Correspondence: Jianqing Li, ; Chengyu Liu,
| | | | | | - Chengyu Liu
- *Correspondence: Jianqing Li, ; Chengyu Liu,
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44
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Cai Z, Wang T, Shen Y, Xing Y, Yan R, Li J, Liu C. Robust PVC Identification by Fusing Expert System and Deep Learning. BIOSENSORS 2022; 12:185. [PMID: 35448245 PMCID: PMC9025768 DOI: 10.3390/bios12040185] [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: 02/27/2022] [Revised: 03/18/2022] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.
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Affiliation(s)
- Zhipeng Cai
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Tiantian Wang
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Yumin Shen
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Yantao Xing
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Ruqiang Yan
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 714009, China;
| | - Jianqing Li
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 714009, China;
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
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45
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Mercer R, Alaee S, Abdoli A, Senobari NS, Singh S, Murillo A, Keogh E. Introducing the contrast profile: a novel time series primitive that allows real world classification. Data Min Knowl Discov 2022. [DOI: 10.1007/s10618-022-00824-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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46
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Automatic classification of electrocardiogram signals based on transfer learning and continuous wavelet transform. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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47
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Zhu W, Ma G, Zheng L, Chen Y, Qiu L, Wang L. Inter-patient arrhythmia identification method with RR-intervals and convolutional neural networks. Physiol Meas 2022; 43. [PMID: 35213844 DOI: 10.1088/1361-6579/ac58de] [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: 11/15/2021] [Accepted: 02/25/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The arrhythmia identification method based on the U-net has the potential for fast application. The RR-intervals have been proven to improve the performance of single-heartbeat identification methods. However, because both the heartbeats number and location in the input of the U-net are unfixed, the approach based on the U-net cannot use RR-intervals directly. To solve this problem, we proposed a novel method. The proposed method also can identify heartbeats of four classes, including Non-ectopic (N), Supraventricular ectopic beat (SVEB), Ventricular ectopic beat (VEB), and Fusion beat (F). APPROACH Our method consists of the pre-processing and the two-stage identification framework. In the pre-processing part, we filtered input signals with a band-pass filter and created the auxiliary waveforms by RR-intervals. In the first stage of the framework, we designed a network to handle input signals and auxiliary waveforms. We proposed a masking operation to separate the input signal into two signals according to the result of the network. The first signal contains heartbeats of SVEB and VEB. The second signal includes heartbeats of N and F. The second stage consists of two networks and can further identify the heartbeats of SVEB, VEB, N, and F from these two signals. MAIN RESULT We validated our method on the MIT-BIH arrhythmia database with the inter-patient model. For classes N, SVEB, VEB, and F, our approach achieved F1 scores of 98.26, 68.61, 95.99, and 47.75, respectively. Significant. Our method not only can effectively utilize RR intervals but also can identify multiple arrhythmias.
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Affiliation(s)
- Wenliang Zhu
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, Anhui, 230026, CHINA
| | - Gang Ma
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, Anhui, 230026, CHINA
| | - Lesong Zheng
- School of Electronics and Information Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215000, CHINA
| | - Yuhang Chen
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, Anhui, 230026, CHINA
| | - Lishen Qiu
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, 230026, CHINA
| | - Lirong Wang
- Soochow University, No.1 Shizi Road, Suzhou, 215000, CHINA
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48
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Siouda R, Nemissi M, Seridi H. A random deep neural system for heartbeat classification. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09429-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Kaya Y, Kuncan F, Tekin R. A New Approach for Congestive Heart Failure and Arrhythmia Classification Using Angle Transformation with LSTM. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-06617-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Du C, Liu PX, Zheng M. Classification of Imbalanced Electrocardiosignal Data using Convolutional Neural Network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106483. [PMID: 34871837 DOI: 10.1016/j.cmpb.2021.106483] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 10/14/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE In the application of wearable heart-monitors, it is of great significance to analyze electrocardiogram (ECG) signals for anomaly detection. ECG arrhythmia classification remains an open problem in that it cannot easily recognize data from minority classes due to the imbalanced dataset and particular characteristic of the time series signal. In this study, a novel method is presented as a possible solution to imbalanced classification problems. METHODS An improved data augmentation method based on variational auto-encoder (VAE) and auxiliary classifier generative adversarial network (ACGAN) is implemented to address the difficulties resulting from the imbalanced dataset. Based on the augmented dataset, convolutional neural network (CNN) classifiers are employed to automatically recognize arrhythmias using two-dimensional ECG images. RESULTS In experimental studies conducted with the MIT-BIH arrhythmia database, the proposed method achieves 98.45% accuracy and 97.03% sensitivity. The sensitivities of two minority classes achieve 95.83% and 97.37%, respectively. CONCLUSION In imbalanced classification, the sensitivity of minority class is a key evaluation indicator. One of the significant contributions of this study is that the proposed method can obtain higher sensitivity of minority class. The experimental results demonstrate that the proposed method for ECG arrhythmia calssification under imbalanced data has better performance compared with traditional cropping augmentation methods and traditional classifiers.
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Affiliation(s)
- Chaofan Du
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China.
| | - Peter Xiaoping Liu
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada.
| | - Minhua Zheng
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China; Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology (Beijing Jiaotong University), Ministry of Education, P. R. China.
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