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Cai J, Song J, Peng B. Enhancing ECG Heartbeat classification with feature fusion neural networks and dynamic minority-biased batch weighting loss function. Physiol Meas 2024; 45:075002. [PMID: 38936397 DOI: 10.1088/1361-6579/ad5cc0] [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: 02/04/2024] [Accepted: 06/27/2024] [Indexed: 06/29/2024]
Abstract
Objective.This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by significant imbalances in ECG data.Approach.We propose a feature fusion neural network enhanced by a dynamic minority-biased batch weighting loss function. This network comprises three specialized branches: the complete ECG data branch for a comprehensive view of ECG signals, the local QRS wave branch for detailed features of the QRS complex, and theRwave information branch to analyzeRwave characteristics. This structure is designed to extract diverse aspects of ECG data. The dynamic loss function prioritizes minority classes while maintaining the recognition of majority classes, adjusting the network's learning focus without altering the original data distribution. Together, this fusion structure and adaptive loss function significantly improve the network's ability to distinguish between various heartbeat classes, enhancing the accuracy of minority class identification.Main results.The proposed method demonstrated balanced performance within the MIT-BIH dataset, especially for minority classes. Under the intra-patient paradigm, the accuracy, sensitivity, specificity, and positive predictive value for Supraventricular ectopic beat were 99.63%, 93.62%, 99.81%, and 92.98%, respectively, and for Fusion beat were 99.76%, 85.56%, 99.87%, and 84.16%, respectively. Under the inter-patient paradigm, these metrics were 96.56%, 89.16%, 96.84%, and 51.99%for Supraventricular ectopic beat, and 96.10%, 77.06%, 96.25%, and 13.92%for Fusion beat, respectively.Significance.This method effectively addresses the class imbalance in ECG datasets. By leveraging diverse ECG signal information and a novel loss function, this approach offers a promising tool for aiding in the diagnosis and treatment of cardiac conditions.
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Affiliation(s)
- Jiajun Cai
- School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, Sichuan, People's Republic of China
| | - Junmei Song
- Department of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, People's Republic of China
- Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, People's Republic of China
| | - Bo Peng
- School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, Sichuan, People's Republic of China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 641400, Sichuan, People's Republic of China
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2
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Mandala S, Rizal A, Adiwijaya, Nurmaini S, Suci Amini S, Almayda Sudarisman G, Wen Hau Y, Hanan Abdullah A. An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG). PLoS One 2024; 19:e0297551. [PMID: 38593145 PMCID: PMC11003640 DOI: 10.1371/journal.pone.0297551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 01/09/2024] [Indexed: 04/11/2024] Open
Abstract
Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.
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Affiliation(s)
- Satria Mandala
- Human Centric (HUMIC) Engineering, Telkom University, Bandung, Indonesia
- School of Computing, Telkom University, Bandung, Indonesia
| | - Ardian Rizal
- Department of Cardiology and Vascular Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, Indonesia
| | - Adiwijaya
- Human Centric (HUMIC) Engineering, Telkom University, Bandung, Indonesia
- School of Computing, Telkom University, Bandung, Indonesia
| | - Siti Nurmaini
- Intelligent System Research Group, Universitas Sriwijaya, Palembang, South Sumatra, Indonesia
| | | | | | - Yuan Wen Hau
- IJN-UTM Cardiovascular Engineering Centre, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
| | - Abdul Hanan Abdullah
- Human Centric (HUMIC) Engineering, Telkom University, Bandung, Indonesia
- Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
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3
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Chaubey K, Saha S. Electrocardiogram morphological arrhythmia classification using fuzzy entropy-based feature selection and optimal classifier. Biomed Phys Eng Express 2023; 9:065015. [PMID: 37604128 DOI: 10.1088/2057-1976/acf222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 08/21/2023] [Indexed: 08/23/2023]
Abstract
Electrocardiogram (ECG) signal analysis has become significant in recent years as cardiac arrhythmia shares a major portion of all mortality worldwide. To detect these arrhythmias, computer-assisted algorithms play a pivotal role as beat-by-beat monitoring of holter ECG signals is required. In this paper, a morphological arrhythmia classification algorithm has been proposed to classify seven different ECG beats, namely Normal Beat (N), Left Bundle Branch Block Beat (L), Right Bundle Branch Block Beat (R), Atrial Premature Contraction Beat (A), Premature Ventricular Contraction Beat (V), Fusion of Normal and Ventricle Beat (F) and Pace Beat (P). A novel feature set of 25 attributes has been extracted from each ECG beat and ranked using the Fuzzy Entropy-based feature selection (FEBFS) technique. In addition, two distinct classifiers, support vector machine with radial basis function as the kernel (SVM-RBF) and weighted K-nearest neighbor (WKNN), are used to categorize ECG beats, and their performances are also evaluated after adjusting vital parameters. The performance of classifiers is compared for four different ECG beat segmentation approaches and further analyzed using three similarity measurement techniques and two fuzzy entropy methods while feature selection. The classifier results are also cross-validated using a 10-fold cross-validation scheme, and the MIT-BIH Arrhythmia Database has been used to validate the proposed work. After selecting 21 highly ranked features, WKNN achieves the best results with the nearest neighbor value K = 3 and cityblock distance metrics, with Average Sensitivity (Sen) = 94.89%, Positive Predictivity (Ppre) = 97.13%, Specificity (Spe) = 99.72%, F1 Score = 95.95%, and Overall Accuracy (Acc) = 99.15%. The novelty of this work relies on formulating a unique feature set, including proposed symbolic features, followed by the FEBFS technique making this algorithm efficient and reliable for morphological arrhythmia classification. The above results demonstrate that the proposed algorithm performs better than many existing state-of-the-art works.
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Affiliation(s)
- Krishnakant Chaubey
- Department of Electronics & Communication Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna, 800005, Bihar, India
| | - Seemanti Saha
- Department of Electronics & Communication Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna, 800005, Bihar, India
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4
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Varghese A, Kamal S, Kurian J. Transformer-based temporal sequence learners for arrhythmia classification. Med Biol Eng Comput 2023; 61:1993-2000. [PMID: 37278886 DOI: 10.1007/s11517-023-02858-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 05/25/2023] [Indexed: 06/07/2023]
Abstract
An electrocardiogram (ECG) plays a crucial role in identifying and classifying cardiac arrhythmia. Traditional methods employ handcrafted features, and more recently, deep learning methods use convolution and recursive structures to classify heart signals. Considering the time sequence nature of the ECG signal, a transformer-based model with its high parallelism is proposed to classify ECG arrhythmia. The DistilBERT transformer model, pre-trained for natural language processing tasks, is used in the proposed work. The signals are denoised and then segmented around the R peak and oversampled to get a balanced dataset. The input embedding step is skipped, and only positional encoding is done. The final probabilities are obtained by adding a classification head to the transformer encoder output. The experiments on the MIT-BIH dataset show that the suggested model is excellent in classifying various arrhythmias. The model achieved 99.92% accuracy, 0.99 precision, sensitivity, and F1 score on the augmented dataset with a ROC-AUC score of 0.999.
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Affiliation(s)
- Ann Varghese
- Department of Electronics, Cochin University of Science and Technology, Cochin, 682022, Kerala, India.
| | - Suraj Kamal
- Department of Electronics, Cochin University of Science and Technology, Cochin, 682022, Kerala, India
| | - James Kurian
- Department of Electronics, Cochin University of Science and Technology, Cochin, 682022, Kerala, India
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5
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Jang KJ, Dutta S, Park J, Weimer J, Lee I. Memory Classifiers for Robust ECG Classification against Physiological Noise. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083527 DOI: 10.1109/embc40787.2023.10339980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The development of sophisticated machine learning algorithms has made it possible to detect critical health conditions like cardiac arrhythmia, directly from electrocardiogram (ECG) recordings. Large-scale machine learning models, like deep neural networks, are well known to underperform when subjected to small perturbations which would not pose a challenge to physicians. This is a hurdle that needs to be removed to facilitate wide-scale adoption. We find this to be true even for models trained using data-augmentation schemes.In this paper, we show that using memory classifiers it is possible to attain a boost in robustness using expert-informed features. Memory classifiers combine standard deep neural network training with a domain knowledge-guided similarity metric to boost the robustness of classifiers. We evaluate the performance of the models against naturally occurring physiological perturbations, specifically electrode movement, muscle artifact, and baseline wander noise. Our approach demonstrates improved robustness across all evaluated noises for an average improvement in F1 score of 3.13% compared to models using data augmentation techniques.Clinical relevance- This approach improves the robustness of deep learning methods in safety-critical medical applications.
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6
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A fully automatic model for premature ventricular heartbeat arrhythmia classification using the Internet of Medical Things. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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7
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Geng Q, Liu H, Gao T, Liu R, Chen C, Zhu Q, Shu M. An ECG Classification Method Based on Multi-Task Learning and CoT Attention Mechanism. Healthcare (Basel) 2023; 11:healthcare11071000. [PMID: 37046927 PMCID: PMC10094198 DOI: 10.3390/healthcare11071000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/25/2023] [Accepted: 03/26/2023] [Indexed: 04/03/2023] Open
Abstract
Electrocardiogram (ECG) is an efficient and simple method for the diagnosis of cardiovascular diseases and has been widely used in clinical practice. Because of the shortage of professional cardiologists and the popularity of electrocardiograms, accurate and efficient arrhythmia detection has become a hot research topic. In this paper, we propose a new multi-task deep neural network, which includes a shared low-level feature extraction module (i.e., SE-ResNet) and a task-specific classification module. Contextual Transformer (CoT) block is introduced in the classification module to dynamically model the local and global information of ECG feature sequence. The proposed method was evaluated on public CPSC2018 and PTB-XL datasets and achieved an average F1 score of 0.827 on the CPSC2018 dataset and an average F1 score of 0.833 on the PTB-XL dataset.
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Affiliation(s)
- Quancheng Geng
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Hui Liu
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Tianlei Gao
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Rensong Liu
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Chao Chen
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Qing Zhu
- Department of Cardiology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Minglei Shu
- Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
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8
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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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9
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Yang M, Zhang H, Liu W, Yong K, Xu J, Luo Y, Zhang H. Knowledge graph analysis and visualization of artificial intelligence applied in electrocardiogram. Front Physiol 2023; 14:1118360. [PMID: 36846320 PMCID: PMC9947408 DOI: 10.3389/fphys.2023.1118360] [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: 12/07/2022] [Accepted: 01/31/2023] [Indexed: 02/11/2023] Open
Abstract
Background: Electrocardiogram (ECG) provides a straightforward and non-invasive approach for various applications, such as disease classification, biometric identification, emotion recognition, and so on. In recent years, artificial intelligence (AI) shows excellent performance and plays an increasingly important role in electrocardiogram research as well. Objective: This study mainly adopts the literature on the applications of artificial intelligence in electrocardiogram research to focus on the development process through bibliometric and visual knowledge graph methods. Methods: The 2,229 publications collected from the Web of Science Core Collection (WoSCC) database until 2021 are employed as the research objects, and a comprehensive metrology and visualization analysis based on CiteSpace (version 6.1. R3) and VOSviewer (version 1.6.18) platform, which were conducted to explore the co-authorship, co-occurrence and co-citation of countries/regions, institutions, authors, journals, categories, references and keywords regarding artificial intelligence applied in electrocardiogram. Results: In the recent 4 years, both the annual publications and citations of artificial intelligence in electrocardiogram sharply increased. China published the most articles while Singapore had the highest ACP (average citations per article). The most productive institution and authors were Ngee Ann Polytech from Singapore and Acharya U. Rajendra from the University of Technology Sydney. The journal Computers in Biology and Medicine published the most influential publications, and the subject with the most published articles are distributed in Engineering Electrical Electronic. The evolution of research hotspots was analyzed by co-citation references' cluster knowledge visualization domain map. In addition, deep learning, attention mechanism, data augmentation, and so on were the focuses of recent research through the co-occurrence of keywords.
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Affiliation(s)
- Mengting Yang
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Collaborative Innovation Center for Prevention of Cardiovascular Diseases, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China,School of Medical Information and Engineering, Southwest Medical University, Luzhou, China,College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Hongchao Zhang
- School of Physical Education, Southwest Medical University, Luzhou, China
| | - Weichao Liu
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Collaborative Innovation Center for Prevention of Cardiovascular Diseases, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
| | - Kangle Yong
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Jie Xu
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Yamei Luo
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Henggui Zhang
- Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Collaborative Innovation Center for Prevention of Cardiovascular Diseases, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China,Department of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom,*Correspondence: Henggui Zhang,
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10
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Song Y, Chen J, Zhang R. Heart Rate Estimation from Incomplete Electrocardiography Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:597. [PMID: 36679394 PMCID: PMC9860828 DOI: 10.3390/s23020597] [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: 11/24/2022] [Revised: 12/23/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60−120 bpm in the database without significant arrhythmias and a corresponding range of 30−150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.
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Affiliation(s)
- Yawei Song
- School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361005, China
| | - Jia Chen
- School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361005, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China
| | - Rongxin Zhang
- Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Xiamen University, Ministry of Education, Xiamen 361005, China
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11
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Khan F, Yu X, Yuan Z, Rehman AU. ECG classification using 1-D convolutional deep residual neural network. PLoS One 2023; 18:e0284791. [PMID: 37098024 PMCID: PMC10128986 DOI: 10.1371/journal.pone.0284791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 04/07/2023] [Indexed: 04/26/2023] Open
Abstract
An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier's performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.
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Affiliation(s)
- Fahad Khan
- School of Automation, Northwestern Polytechnical University, Xi'an, China
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Pakistan
| | - Xiaojun Yu
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Zhaohui Yuan
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Atiq Ur Rehman
- Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden
- Department of Electrical and Computer Engineering, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, Pakistan
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12
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Żmudzka E, Lustyk K, Siwek A, Wolak M, Gałuszka A, Jaśkowska J, Kołaczkowski M, Sapa J, Pytka K. Novel Arylpiperazine Derivatives of Salicylamide with α 1-Adrenolytic Properties Showed Antiarrhythmic and Hypotensive Properties in Rats. Int J Mol Sci 2022; 24:ijms24010293. [PMID: 36613736 PMCID: PMC9820316 DOI: 10.3390/ijms24010293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/09/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Cardiovascular diseases remain one of the leading causes of death worldwide. Unfortunately, the available pharmacotherapeutic options have limited effectiveness. Therefore, developing new drug candidates remains very important. We selected six novel arylpiperazine alkyl derivatives of salicylamide to investigate their cardiovascular effects. Having in mind the beneficial role of α1-adrenergic receptors in restoring sinus rhythm and regulating blood pressure, first, using radioligand binding assays, we evaluated the affinity of the tested compounds for α-adrenergic receptors. Our experiments revealed their high to moderate affinity for α1- but not α2-adrenoceptors. Next, we aimed to determine the antiarrhythmic potential of novel derivatives in rat models of arrhythmia induced by adrenaline, calcium chloride, or aconitine. All compounds showed potent prophylactic antiarrhythmic activity in the adrenaline-induced arrhythmia model and no effects in calcium chloride- or aconitine-induced arrhythmias. Moreover, the tested compounds demonstrated therapeutic antiarrhythmic activity, restoring a normal sinus rhythm immediately after the administration of the arrhythmogen adrenaline. Notably, none of the tested derivatives affected the normal electrocardiogram (ECG) parameters in rodents, which excludes their proarrhythmic potential. Finally, all tested compounds decreased blood pressure in normotensive rats and reversed the pressor response to methoxamine, suggesting that their hypotensive mechanism of action is connected with the blockade of α1-adrenoceptors. Our results confirm the antiarrhythmic and hypotensive activities of novel arylpiperazine derivatives and encourage their further investigation as model structures for potential drugs.
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Affiliation(s)
- Elżbieta Żmudzka
- Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, 30-688 Krakow, Poland
- Correspondence:
| | - Klaudia Lustyk
- Department of Pharmacodynamics, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, 30-688 Krakow, Poland
| | - Agata Siwek
- Department of Pharmacobiology, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, 30-688 Krakow, Poland
| | - Małgorzata Wolak
- Department of Pharmacobiology, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, 30-688 Krakow, Poland
| | - Adam Gałuszka
- Department of Automatic Control and Robotics, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland
| | - Jolanta Jaśkowska
- Department of Organic Chemistry and Technology, Faculty of Chemical and Engineering and Technology, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
| | - Marcin Kołaczkowski
- Department of Medicinal Chemistry, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, 30-688 Krakow, Poland
| | - Jacek Sapa
- Department of Pharmacodynamics, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, 30-688 Krakow, Poland
| | - Karolina Pytka
- Department of Pharmacodynamics, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, 30-688 Krakow, Poland
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Xia P, He Z, Bai Z, Wang Y, Yu X, Geng F, Du L, Chen X, Wang P, Zhu Y, Du M, Fang Z. A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs. Physiol Meas 2022; 43. [PMID: 36336789 DOI: 10.1088/1361-6579/ac7695] [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: 01/10/2022] [Accepted: 06/07/2022] [Indexed: 11/09/2022]
Abstract
Objective. The ECG is a standard diagnostic tool for identifying many arrhythmias. Accurate diagnosis and early intervention for arrhythmias are of great significance to the prevention and treatment of cardiovascular disease. Our objective is to develop an algorithm that can automatically identify 30 arrhythmias by using varying-dimensional ECG signals.Approach. In this paper, we firstly proposed a novel multi-scale 2D CNN that can effectively capture pathological information from small-scale to large-scale from ECG signals to identify 30 arrhythmias from 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs. Secondly, we explored the effects of varying convolution kernels sizes and branch subnetworks on the model's performance for each arrhythmia. Thirdly, we introduced the weighted focal loss to alleviate the positive-negative class imbalance problem in the multi-label arrhythmias classification. Fourthly, we explored the utility of reduced-lead ECGs in detecting arrhythmias by comparing the performances of models on varying-dimensional ECGs.Main results. As a follow-up entry after the PhysioNet/Computing in Cardiology Challenge (2021), our proposed approach achieved the official test scores of 0.52, 0.47, 0.53, 0.51, and 0.50 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs on the hidden test set (comparable to that of 6th, 11th, 4th, 5th, and 7th out of 39 teams in the Challenge).Significance. A multi-scale framework capable of detecting 30 arrhythmias from varying-dimensional ECGs was proposed in our work. We preliminarily verified that the multi-scale perception fields may be necessary to capture more comprehensive pathological information for arrhythmias detection. Besides, we also verified that the weighted focal loss may alleviate the positive-negative class imbalance and improve the model's generalization performance on the cross-dataset. In addition, we observed that some reduced-lead models, such as the 4-lead and 3-lead models, can even achieve performance that is almost comparable to that of the 12-lead model. The excellent performance of our proposed framework demonstrates its great potential in detecting a wide range of arrhythmias.
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Affiliation(s)
- Pan Xia
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Zhengling He
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Zhongrui Bai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Yuqi Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Xianya Yu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Fanglin Geng
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Lidong Du
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Xianxiang Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Peng Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Yusi Zhu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Mingyan Du
- Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Zhen Fang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China.,Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing, People's Republic of China
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14
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An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9475162. [PMID: 36210977 PMCID: PMC9536938 DOI: 10.1155/2022/9475162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/25/2022] [Accepted: 08/17/2022] [Indexed: 01/10/2023]
Abstract
Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart's rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors' workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.
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15
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The Antiarrhythmic and Hypotensive Effects of S-61 and S-73, the Pyrrolidin-2-one Derivatives with α1-Adrenolytic Properties. Int J Mol Sci 2022; 23:ijms231810381. [PMID: 36142287 PMCID: PMC9499458 DOI: 10.3390/ijms231810381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 12/02/2022] Open
Abstract
Heart rhythm abnormalities are a cause of many deaths worldwide. Unfortunately, the available antiarrhythmic drugs show limited efficacy and proarrhythmic potential. Thus, efforts should be made to search for new, more effective, and safer pharmacotherapies. Several studies suggested that blocking the α1-adrenoceptors could restore normal heart rhythm in arrhythmia. In this study, we aimed to assess the antiarrhythmic potential of S-61 and S-73, two novel pyrrolidin-2-one derivatives with high affinity for α1-adrenergic receptors. First, using radioligand binding studies, we demonstrated that S-61 and S-73 did not bind with β1-adrenoceptors. Next, we assessed whether S-61 and S-73 could protect rats against arrhythmia in adrenaline-, calcium chloride- and aconitine-induced arrhythmia models. Both compounds showed potent prophylactic antiarrhythmic properties in the adrenaline-induced arrhythmia model, but the effect of S-61 was more pronounced. None of the compounds displayed antiarrhythmic effects in calcium chloride- or aconitine-induced arrhythmia models. Interestingly, both derivatives revealed therapeutic antiarrhythmic activity in the adrenaline-induced arrhythmia, diminishing heart rhythm irregularities. Neither S-61 nor S-73 showed proarrhythmic potential in rats. Finally, the compounds decreased blood pressure in rodents. The hypotensive effects were not observed after coadministration with methoxamine, which suggests the α1-adrenolytic properties of both compounds. Our results confirm that pyrrolidin-2-one derivatives possess potent antiarrhythmic properties. Given the promising results of our experiments, further studies on pyrrolidin-2-one derivatives might result in the development of a new class of antiarrhythmic drugs.
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16
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A Framework on Performance Analysis of Mathematical Model-Based Classifiers in Detection of Epileptic Seizure from EEG Signals with Efficient Feature Selection. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7654666. [PMID: 36110984 PMCID: PMC9470336 DOI: 10.1155/2022/7654666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 07/22/2022] [Accepted: 08/10/2022] [Indexed: 11/17/2022]
Abstract
Epilepsy is one of the neurological conditions that are diagnosed in the vast majority of patients. Electroencephalography (EEG) readings are the primary tool that is used in the process of diagnosing and analyzing epilepsy. The epileptic EEG data display the electrical activity of the neurons and provide a significant amount of knowledge on pathology and physiology. As a result of the significant amount of time that this method requires, several automated classification methods have been developed. In this paper, three wavelets such as Haar, dB4, and Sym 8 are employed to extract the features from A–E sets of the Bonn epilepsy dataset. To select the best features of epileptic seizures, a Particle Swarm Optimization (PSO) technique is applied. The extracted features are further classified using seven classifiers like linear regression, nonlinear regression, Gaussian Mixture Modeling (GMM), K-Nearest Neighbor (KNN), Support Vector Machine (SVM-linear), SVM (polynomial), and SVM Radial Basis Function (RBF). Classifier performances are analyzed through the benchmark parameters, such as sensitivity, specificity, accuracy, F1 Score, error rate, and g-means. The SVM classifier with RBF kernel in sym 8 wavelet features with PSO feature selection method attains a higher accuracy rate of 98% with an error rate of 2%. This classifier outperforms all other classifiers.
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17
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Li J, Pang SP, Xu F, Ji P, Zhou S, Shu M. Two-dimensional ECG-based cardiac arrhythmia classification using DSE-ResNet. Sci Rep 2022; 12:14485. [PMID: 36008568 PMCID: PMC9411603 DOI: 10.1038/s41598-022-18664-0] [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: 04/12/2022] [Accepted: 08/17/2022] [Indexed: 11/09/2022] Open
Abstract
Electrocardiogram (ECG) is mostly used for the clinical diagnosis of cardiac arrhythmia due to its simplicity, non-invasiveness, and reliability. Recently, many models based on the deep neural networks have been applied to the automatic classification of cardiac arrhythmia with great success. However, most models independently extract the internal features of each lead in the 12-lead ECG during the training phase, resulting in a lack of inter-lead features. Here, we propose a general model based on the two-dimensional ECG and ResNet with detached squeeze-and-excitation modules (DSE-ResNet) to realize the automatic classification of normal rhythm and 8 cardiac arrhythmias. The original 12-lead ECG is spliced into a two-dimensional plane like a grayscale picture. DSE-ResNet is used to simultaneously extract the internal and inter-lead features of the two-dimensional ECG. Furthermore, an orthogonal experiment method is used to optimize the hyper-parameters of DSE-ResNet and a multi-model voting strategy is used to improve classification performance. Experimental results based on the test set of China Physiological Signal Challenge 2018 (CPSC2018) show that our model has average \documentclass[12pt]{minimal}
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\begin{document}$$F_1= 0.817$$\end{document}F1=0.817 for classifying normal rhythm and 8 cardiac arrhythmias. Meanwhile, compared with the state-of-art model in CPSC2018, our model achieved the best \documentclass[12pt]{minimal}
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\begin{document}$$F_1$$\end{document}F1 in 2 sub-abnormal types. This shows that the model based on the two-dimensional ECG and DSE-ResNet has advantage in detecting some cardiac arrhythmias and has the potential to be used as an auxiliary tool to help doctors perform cardiac arrhythmias analysis.
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Affiliation(s)
- Jiahao Li
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Science), Jinan, 250353, Shandong Province, China
| | - Shao-Peng Pang
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Science), Jinan, 250353, Shandong Province, China.
| | - Fangzhou Xu
- School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Science), Jinan, 250353, Shandong Province, China
| | - Peng Ji
- School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Science), Jinan, 250353, Shandong Province, China
| | - Shuwang Zhou
- Qilu University of Technology (Shandong Academy of Sciences), Shandong Artificial Intelligence Institute, Jinan, 250014, China
| | - Minglei Shu
- Qilu University of Technology (Shandong Academy of Sciences), Shandong Artificial Intelligence Institute, Jinan, 250014, China.
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18
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Pramukantoro ES, Gofuku A. A Heartbeat Classifier for Continuous Prediction Using a Wearable Device. SENSORS 2022; 22:s22145080. [PMID: 35890769 PMCID: PMC9320854 DOI: 10.3390/s22145080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/22/2022] [Accepted: 07/04/2022] [Indexed: 02/06/2023]
Abstract
Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.
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Affiliation(s)
- Eko Sakti Pramukantoro
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, 3-1-1 Tsushimanaka, Kita-Ku, Okayama 700-8530, Japan
- Faculty of Computer Science, Brawijaya University, Malang 65145, Indonesia
- Correspondence: (E.S.P.); (A.G.)
| | - Akio Gofuku
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, 3-1-1 Tsushimanaka, Kita-Ku, Okayama 700-8530, Japan
- Correspondence: (E.S.P.); (A.G.)
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19
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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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20
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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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21
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Automated Detection of Left Bundle Branch Block from ECG Signal Utilizing the Maximal Overlap Discrete Wavelet Transform with ANFIS. COMPUTERS 2022. [DOI: 10.3390/computers11060093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Left bundle branch block (LBBB) is a common disorder in the heart’s electrical conduction system that leads to the ventricles’ uncoordinated contraction. The complete LBBB is usually associated with underlying heart failure and other cardiac diseases. Therefore, early automated detection is vital. This work aimed to detect the LBBB through the QRS electrocardiogram (ECG) complex segments taken from the MIT-BIH arrhythmia database. The used data contain 2655 LBBB (abnormal) and 1470 normal signals (i.e., 4125 total signals). The proposed method was employed in the following steps: (i) QRS segmentation and filtration, (ii) application of the Maximal Overlapped Discrete Wavelet Transform (MODWT) on the ECG R wave, (iii) selection of the detailed coefficients of the MODWT (D2, D3, D4), kurtosis, and skewness as extracted features to be fed into the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier. The obtained results proved that the proposed method performed well based on the achieved sensitivity, specificity, and classification accuracies of 99.81%, 100%, and 99.88%, respectively (F-Score is equal to 0.9990). Our results showed that the proposed method was robust and effective and could be used in real clinical situations.
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22
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Liang X, Li H, Vuckovic A, Mercer J, Heidari H. A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection. IEEE Trans Biomed Eng 2022; 69:1837-1849. [PMID: 34797760 DOI: 10.1109/tbme.2021.3129306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signal. This model aims to develop a hardware-based signal processing model and avoid employing digitally intensive operations, such as signal segmentation and feature extraction, which are not desired in an analogue neuromorphic system. We apply delay-based reservoir computing as the information processing core, along with a novel training and labelling method. Different from the conventional ECG classification techniques, this computation model is a end-to-end dynamic system that mimics the real-time signal flow in neuromorphic hardware. The input is the raw ECG stream, while the amplitude of the output represents the risk factor of a ventricular ectopic heartbeat. The intrinsic memristive property of the reservoir empowers the system to retain the historical ECG information for high-dimensional mapping. This model was evaluated with the MIT-BIH database under the inter-patient paradigm and yields 81% sensitivity and 98% accuracy. Under this architecture, the minimum size of memory required in the inference process can be as low as 3.1 MegaByte(MB) because the majority of the computation takes place in the analogue domain. Such computational modelling boosts memory efficiency by simplifying the computing procedure and minimizing the required memory for future wearable devices.
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Affiliation(s)
- Xiangpeng Liang
- James Watt School of Engineering, University of Glasgow, U.K
| | - Haobo Li
- James Watt School of Engineering, University of Glasgow, U.K
| | | | - John Mercer
- BHF Cardiovascular Research Centre, University of Glasgow, U.K
| | - Hadi Heidari
- James Watt School of Engineering, University of Glasgow, Glasgow, U.K
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23
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Yakut Ö, Bolat ED. A high-performance arrhythmic heartbeat classification using ensemble learning method and PSD based feature extraction approach. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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24
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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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25
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Eltrass AS, Tayel MB, Ammar AI. Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06889-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
AbstractElectrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-class classification. The proposed system enhances the ECG diagnosis performance by combining optimized deep learning features with an effective aggregation of ECG features and HRV measures using chaos theory and fragmentation analysis. The constant-Q non-stationary Gabor transform technique is employed to convert the 1-D ECG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called AlexNet. The pair-wise feature proximity algorithm is employed to select the optimal features from the AlexNet output feature vector to be concatenated with the ECG and HRV measures. The concatenated features are sent to different types of classifiers to distinguish three distinct subjects, namely congestive heart failure, arrhythmia, and normal sinus rhythm (NSR). The results reveal that the linear discriminant analysis classifier has the highest accuracy compared to the other classifiers. The proposed system is investigated with real ECG data taken from well-known databases, and the experimental results show that the proposed diagnosis system outperforms other recent state-of-the-art systems in terms of accuracy 98.75%, specificity 99.00%, sensitivity of 98.18%, and computational time 0.15 s. This demonstrates that the proposed system can be used to assist cardiologists in enhancing the accuracy of ECG diagnosis in real-time clinical setting.
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26
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Li W, Xu Z, Wang H, Wu T. The Effect of Electrical Stimulation on the Response of Mouse Retinal Ganglion Cells. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5704-5708. [PMID: 34892416 DOI: 10.1109/embc46164.2021.9630580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Retinal prostheses can restore the basic visual function of patients with retinal degeneration, which relies on effective electrical stimulation to evoke the physiological activities of retinal ganglion cells (RGCs). Current electrical stimulation strategies suffer from unstable effects and insufficient stimulation positions. Therefore, it is crucial to determine the optimal parameters for precise and safe electrical stimulation. Biphasic voltages (cathode-first) with a pulse width of 25 ms and different amplitudes were used to ex vivo stimulate RGCs of three wild-type (WT) mice using a commercial microelectrode array (MEA) recording system. Based on a facile and efficient spike sorting method, comprehensive statistics of RGCs response types were performed, and the influence of electrical stimulation on RGCs response status was analyzed. There were three types of RGCs response measured from the retinas of three WT mice, and the proportions were calculated to be 91.5%, 3.11% and 5.39%, respectively. This work can provide an in-depth understanding of the internal effects of electrical stimulation and RGCs response, with the potential as a useful guidance for optimizing parameters of electrical stimulation strategies in retinal prostheses.
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27
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Zuzarte I, Sternad D, Paydarfar D. Predicting apneic events in preterm infants using cardio-respiratory and movement features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106321. [PMID: 34380078 PMCID: PMC8898595 DOI: 10.1016/j.cmpb.2021.106321] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 07/25/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Preterm neonates are prone to episodes of apnea, bradycardia and hypoxia (ABH) that can lead to neurological morbidities or even death. There is broad interest in developing methods for real-time prediction of ABH events to inform interventions that prevent or reduce their incidence and severity. Using advances in machine learning methods, this study develops an algorithm to predict ABH events. METHODS Following previous studies showing that respiratory instabilities are closely associated with bouts of movement, we present a modeling framework that can predict ABH events using both movement and cardio-respiratory features derived from routine clinical recordings. In 10 preterm infants, movement onsets and durations were estimated with a wavelet-based algorithm that quantified artifactual distortions of the photoplethysmogram signal. For prediction, cardio-respiratory features were created from time-delayed correlations of inter-beat and inter-breath intervals with past values; movement features were derived from time-delayed correlations with inter-breath intervals. Gaussian Mixture Models and Logistic Regression were used to develop predictive models of apneic events. Performance of the models was evaluated with ROC curves. RESULTS Performance of the prediction framework (mean AUC) was 0.77 ± 0.04 for 66 ABH events on training data from 7 infants. When grouped by the severity of the associated bradycardia during the ABH event, the framework was able to predict 83% and 75% of the most severe episodes in the 7-infant training set and 3-infant test set, respectively. Notably, inclusion of movement features significantly improved the predictions compared with modeling with only cardio-respiratory signals. CONCLUSIONS Our findings suggest that recordings of movement provide important information for predicting ABH events in preterm infants, and can inform preemptive interventions designed to reduce the incidence and severity of ABH events.
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Affiliation(s)
- Ian Zuzarte
- Department of Bioengineering, Northeastern University, Boston, MA 02115, United States
| | - Dagmar Sternad
- Departments of Biology, Electrical and Computer Engineering & Physics, Northeastern University, Boston, MA 02115, United States
| | - David Paydarfar
- Department of Neurology, Dell Medical School, Austin, TX 78712, United States; Oden Institute for Computational Sciences and Engineering, The University of Texas at Austin, Austin, TX 78712, United States.
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28
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S. A, S. V. Machine learning based pervasive analytics for ECG signal analysis. INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2021. [DOI: 10.1108/ijpcc-03-2021-0080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
Pervasive analytics act as a prominent role in computer-aided prediction of non-communicating diseases. In the early stage, arrhythmia diagnosis detection helps prevent the cause of death suddenly owing to heart failure or heart stroke. The arrhythmia scope can be identified by electrocardiogram (ECG) report.
Design/methodology/approach
The ECG report has been used extensively by several clinical experts. However, diagnosis accuracy has been dependent on clinical experience. For the prediction methods of computer-aided heart disease, both accuracy and sensitivity metrics play a remarkable part. Hence, the existing research contributions have optimized the machine-learning approaches to have a great significance in computer-aided methods, which perform predictive analysis of arrhythmia detection.
Findings
In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.
Originality/value
In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.
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Zeng W, Yuan C. ECG arrhythmia classification based on variational mode decomposition, Shannon energy envelope and deterministic learning. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01389-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9913127. [PMID: 34336169 PMCID: PMC8289583 DOI: 10.1155/2021/9913127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/20/2021] [Accepted: 05/27/2021] [Indexed: 12/02/2022]
Abstract
Arrhythmia is a common cardiovascular disease that can threaten human life. In order to assist doctors in accurately diagnosing arrhythmia, an intelligent heartbeat classification system based on the selected optimal feature sets and AdaBoost + Random Forest model is developed. This system can acquire ECG signals through the Holter and transmit them to the cloud platform for preprocessing and feature extraction, and the features are input into AdaBoost + Random Forest for heartbeat classification. The analysis results are output in the form of reports. In this system, by comparing and analyzing the classification accuracy of different feature sets and classifiers, the optimal classification algorithm is obtained and applied to the system. The algorithm accuracy of the system is tested based on the MIT-BIH data set. The result shows that AdaBoost + Random Forest achieved 99.11% accuracy with optimal feature sets. The intelligent heartbeat classification system based on this algorithm has also achieved good results on clinical data.
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Cardiomyopathy -induced arrhythmia classification and pre-fall alert generation using Convolutional Neural Network and Long Short-Term Memory model. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-020-00454-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Interpatient ECG Heartbeat Classification with an Adversarial Convolutional Neural Network. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9946596. [PMID: 34194685 PMCID: PMC8181174 DOI: 10.1155/2021/9946596] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/18/2021] [Indexed: 12/02/2022]
Abstract
Discovering shared, invariant feature representations across subjects in electrocardiogram (ECG) classification tasks is crucial for improving the generalization of models to unknown patients. Although deep neural networks have recently been emerging in extracting generalizable ECG features, they usually rely on labeled samples from a large number of subjects to guarantee generalization. Extracting invariant representations to intersubject variabilities from a small number of subjects is still a challenge today due to individual physical differences. To address this problem, we propose an adversarial deep neural network framework for interpatient heartbeat classification by integrating adversarial learning into a convolutional neural network to learn subject-invariant, class-discriminative features. The proposed method was evaluated on the MIT-BIH arrhythmia database which is a publicly available ECG dataset collected from 47 patients. Compared with the state-of-the-art methods, the proposed method achieves the highest performance for detecting supraventricular ectopic beats (SVEBs), which are very challenging to identify, and also gains comparable performance on the detection of ventricular ectopic beats (VEBs). The sensitivities of SVEBs and VEBs are 78.8% and 92.5%, respectively. The precisions of SVEBs and VEBs are 90.8% and 94.3%, respectively. With high performance in the detection of pathological classes (i.e., SVEBs and VEBs), this work provides a promising method for ECG classification tasks when the number of patients is limited.
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Saini SK, Gupta R. Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-09999-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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MANDAL SAURAV, SINHA NABANITA. ARRHYTHMIA DIAGNOSIS FROM ECG SIGNAL ANALYSIS USING STATISTICAL FEATURES AND NOVEL CLASSIFICATION METHOD. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421500251] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This study aims to present an efficient model for autodetection of cardiac arrhythmia by the diagnosis of self-affinity and identification of governing processes of a number of Electrocardiogram (ECG) signals taken from MIT-BIH database. In this work, the proposed model includes statistical methods to find the diagnosis pattern for detecting cardiac abnormalities which is useful for the computer aided system for arrhythmia detection. First, the Rescale Range (R/S) analysis has been employed for ECG signals to understand the scaling property of ECG signals. The value of Hurst exponent identifies the presence of abnormality in ECG signals taken for consideration with 92.58% accuracy. In this study, Higuchi method which deals with unifractality or monofractality of signals has been applied and it is found that unifractality is sufficient to detect arrhythmia with 91.61% accuracy. The Multifractal Detrended Fluctuation Analysis (MFDFA) has been used over the present signals to identify and confirm the multifractality. The nature of multifractality is different for arrhythmia patients and normal heart condition. The multifractal analysis is useful to detect abnormalities with 93.75% accuracy. Finally, the autocorrelation analysis has been used to identify the prevalent governing process in the present arrhythmic ECG signals and study confirms that all the signals are governed by stationary autoregressive methods of certain orders. In order to increase the overall efficiency, this present model deals with analyzing all the statistical features extracted from different statistical techniques for a large number of ECG signals of normal and abnormal heart condition. Finally, the result of present analysis altogether possibly indicates that the proposed model is efficient to detect cardiac arrhythmia with 99.3% accuracy.
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Affiliation(s)
- SAURAV MANDAL
- Department of Radio Physics and Electronics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata 700009, India
| | - NABANITA SINHA
- Department of Radio Physics and Electronics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata 700009, India
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Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8811837. [PMID: 33575022 PMCID: PMC7861929 DOI: 10.1155/2021/8811837] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 12/09/2020] [Accepted: 01/15/2021] [Indexed: 11/18/2022]
Abstract
Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules. In the preprocessing module, the signal is denoised and segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS length, T length, PR interval, ST segment, QT interval, RR interval, R amplitude, and T amplitude. Subsequently, the features are combined and normalized, and the effect of different feature combinations on heartbeat classification is analyzed to select the optimal feature combination. Finally, the four types of normal and abnormal heartbeats were identified using the Stacking-DWKNN algorithm. This method is performed on the MIT-BIH arrhythmia database. The result shows a sensitivity of 89.42% and a positive predictive value of 94.90% of S-type beats and a sensitivity of 97.21% and a positive predictive value of 97.07% of V-type beats. The obtained average accuracy is 99.01%. Compared to other models with the same features, this method can improve accuracy and has a higher positive predictive value and sensitivity, which is important for clinical decision-making.
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Cardiac Severity Classification Using Pre Trained Neural Networks. Interdiscip Sci 2021; 13:443-450. [PMID: 33481208 DOI: 10.1007/s12539-021-00416-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 12/31/2020] [Accepted: 01/07/2021] [Indexed: 10/22/2022]
Abstract
Electrocardiogram (ECG) is the most effective instrument for making decisions about various forms of heart disease. As a result, several researchers have focused on the ECG signal to extract the features of heartbeats with high precision and efficiency. This article offers a hybrid approach to classifying different cardiac conditions using the Feed Forward Back Propagation Neural Network (FFBPNN), by providing a pre-processed ECG signal as an excitation. The modified ECG signal is obtained through the combination of EMD (Empirical Mode Decomposition) and DWT (Discrete Wavelet Transform). In this proposed method, the input signal is first decomposed into the Intrinsic Mode Functions (IMF's) and the first three IMF's are combined to obtain a modified partially denoted ECG sample and then DWT is used to obtain an improved denoised signal. This pre-processed signal is classified using the Neural Network architecture. For the EMD approach, the ECG-based EMD-DWT signal provides improved classification accuracy of 67, 0762 percent, 90, 4305 percent for the DWT approach, and 95,0797 percent for the proposed technique. The methodology is applied to the MIT-BIH database and, in terms of classification accuracy, is found to be higher than the different methodologies.
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Hammad M, Iliyasu AM, Subasi A, Ho ESL, El-Latif AAA. A Multitier Deep Learning Model for Arrhythmia Detection. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021; 70:1-9. [PMID: 0 DOI: 10.1109/tim.2020.3033072] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
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Rieg T, Frick J, Baumgartl H, Buettner R. Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms. PLoS One 2020; 15:e0243615. [PMID: 33332440 PMCID: PMC7746264 DOI: 10.1371/journal.pone.0243615] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 11/24/2020] [Indexed: 11/18/2022] Open
Abstract
We present the results from a white-box machine learning approach to detect cardiac arrhythmias using electrocardiographic data. A C5.0 is trained to recognize four classes using common features. The four classes are (i) atrial fibrillation and atrial flutter, (ii) tachycardias (iii), sinus bradycardia and (iv) sinus rhythm. Data from 10,646 subjects, 83% of whom have at least one arrhythmia and 17% of whom exhibit a normal sinus rhythm, are used. The C5.0 is trained using 10-fold cross-validation and is able to achieve a balanced accuracy of 95.35%. By using the white-box machine learning approach, a clear and comprehensible tree structure can be revealed, which has selected the 5 most important features from a total of 24 features. These 5 features are ventricular rate, RR-Interval variation, atrial rate, age and difference between longest and shortest RR-Interval. The combination of ventricular rate, RR-Interval variation and atrial rate is especially relevant to achieve classification accuracy, which can be disclosed through the tree. The tree assigns unique values to distinguish the classes. These findings could be applied in medicine in the future. It can be shown that a white-box machine learning approach can reveal granular structures, thus confirming known linear relationships and also revealing nonlinear relationships. To highlight the strength of the C5.0 with respect to this structural revelation, the results of further white-box machine learning and black-box machine learning algorithms are presented.
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Affiliation(s)
- Thilo Rieg
- Machine Learning Research Group, Aalen University, Aalen, Germany
| | - Janek Frick
- Machine Learning Research Group, Aalen University, Aalen, Germany
| | | | - Ricardo Buettner
- Machine Learning Research Group, Aalen University, Aalen, Germany
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Wavelet Transform-Statistical Time Features-Based Methodology for Epileptic Seizure Prediction Using Electrocardiogram Signals. MATHEMATICS 2020. [DOI: 10.3390/math8122125] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Epilepsy is a brain disorder that affects about 50 million persons around the world and is characterized by generating recurrent seizures, which can put patients in permanent because of falls, drowning, burns, and prolonged seizures that they can suffer. Hence, it is of vital importance to propose a methodology with the capability of predicting a seizure with several minutes before the onset, allowing that the patients take their precautions against injuries. In this regard, a methodology based on the wavelet packet transform (WPT), statistical time features (STFs), and a decision tree classifier (DTC) for predicting an epileptic seizure using electrocardiogram (ECG) signals is presented. Seventeen STFs were analyzed to measure changes in the properties of ECG signals and find characteristics capable of differentiating between healthy and 15 min prior to seizure signals. The effectiveness of the proposed methodology for predicting an epileptic event is demonstrated using a database of seven patients with 10 epileptic seizures, which was provided by the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH). The results show that the proposed methodology is capable of predicting an epileptic seizure 15 min before with an accuracy of 100%. Our results suggest that the use of STFs at frequency bands related to heart activity to find parameters for the prediction of epileptic seizures is suitable.
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Al-Yarimi FAM, Munassar NMA, Al-Wesabi FN. Electrocardiogram stream level correlated patterns as features to classify heartbeats for arrhythmia prediction. DATA TECHNOLOGIES AND APPLICATIONS 2020. [DOI: 10.1108/dta-03-2020-0076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeDigital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.Design/methodology/approachConsidering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.FindingsFrom the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.Originality/valueThe authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.
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ECG signal processing and KNN classifier-based abnormality detection by VH-doctor for remote cardiac healthcare monitoring. Soft comput 2020. [DOI: 10.1007/s00500-020-05191-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10144741] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Heart diseases are in the front rank among several kinds of life threats, due to its high incidence and mortality. Regarded as a powerful tool in the diagnosis of the cardiac disorder and arrhythmia detection, analysis of electrocardiogram (ECG) signals has become the focus of numerous researches. In this study, a feature extraction method based on the bispectrum and 2D graph Fourier transform (GFT) was developed. High-order matrix founded on bispectrum are extended into structured datasets and transformed into the eigenvalue spectrum domain by GFT, so that features can be extracted from statistical quantities of eigenvalues. Spectral features have been computed to construct the feature vector. Support vector machine based on the radial basis function kernel (SVM-RBF) was used to classify different arrhythmia heartbeats downloaded from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) Arrhythmia Database, according to the Association for the Advancement of Medical Instrumentation (AAMI) standard. Based on the cross-validation method, the experimental results depicted that our proposed model, the combination of bispectrum and 2D-GFT, achieved a high classification accuracy of 96.2%.
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Mahajan R, Kamaleswaran R, Akbilgic O. Comparative analysis between convolutional neural network learned and engineered features: A case study on cardiac arrhythmia detection. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2020; 1:37-44. [PMID: 35265872 PMCID: PMC8890095 DOI: 10.1016/j.cvdhj.2020.04.001] [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] [Indexed: 11/01/2022] Open
Abstract
Background Atrial fibrillation (AF) is one of the most common cardiovascular problems, and its asymptomatic tendency makes AF detection challenging. Machine and deep learning methods are commonly used in AF detection. Objective The purpose of this study was to evaluate the information provided by convolutional neural network (CNN) and random forest (RF) machine learning models for AF classification. Methods We manually extracted 166 time-frequency domains and linear and nonlinear features to classify single-lead electrocardiograms (ECGs) as normal, AF, other, or noisy sinus rhythms. We selected a subset of 56 robust features using a genetic algorithm that was used in the RF model. In a separate study, a 1-dimensional, 12-layer CNN was designed on the raw ECG rhythms. Four features from the output layer and 128 features from the fully connected layer of CNN were explored independently for classification. The models were trained and internally validated on 8,528 ECGs and externally validated on a hidden dataset containing 3,658 ECGs. Next,we analyzed the correlation between engineered and CNN-learned features. Results An RF classifier trained with 56-engineered features resulted in an F1 score of 0.91, 0.78, and 0.72 for normal, AF, and other rhythms, respectively. However, an ensemble of support vector machine and the CNN model resulted in an F1 score of 0.92, 0.87, and 0.80, respectively. Conclusion We explored various features and machine learning models to identify AF rhythms using short (9-61 seconds) single-lead ECG recordings. Our results showed that the proposed CNN model abstracted distinctive features for AF classification.
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Affiliation(s)
| | | | - Oguz Akbilgic
- Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL
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Cai Z, Li J, Johnson AEW, Zhang X, Shen Q, Zhang J, Liu C. Rule-based rough-refined two-step-procedure for real-time premature beat detection in single-lead ECG. Physiol Meas 2020; 41:054004. [DOI: 10.1088/1361-6579/ab87b4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Zhang J, Liu A, Gao M, Chen X, Zhang X, Chen X. ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. Artif Intell Med 2020; 106:101856. [DOI: 10.1016/j.artmed.2020.101856] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/05/2020] [Accepted: 04/02/2020] [Indexed: 01/16/2023]
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A new BAT optimization algorithm based feature selection method for electrocardiogram heartbeat classification using empirical wavelet transform and Fisher ratio. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01128-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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49
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Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification. ALGORITHMS 2020. [DOI: 10.3390/a13040075] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Finding an optimal combination of features and classifier is still an open problem in the development of automatic heartbeat classification systems, especially when applications that involve resource-constrained devices are considered. In this paper, a novel study of the selection of informative features and the use of a random forest classifier while following the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) and an inter-patient division of datasets is presented. Features were selected using a filter method based on the mutual information ranking criterion on the training set. Results showed that normalized beat-to-beat (R–R) intervals and features relative to the width of the ventricular depolarization waves (QRS complex) are the most discriminative among those considered. The best results achieved on the MIT-BIH Arrhythmia Database were an overall accuracy of 96.14% and F1-scores of 97.97%, 73.06%, and 90.85% in the classification of normal beats, supraventricular ectopic beats, and ventricular ectopic beats, respectively. In comparison with other state-of-the-art approaches tested under similar constraints, this work represents one of the highest performances reported to date while relying on a very small feature vector.
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