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Li H, Han J, Zhang H, Zhang X, Si Y, Zhang Y, Liu Y, Yang H. Clinical knowledge-based ECG abnormalities detection using dual-view CNN-Transformer and external attention mechanism. Comput Biol Med 2024; 178:108751. [PMID: 38936078 DOI: 10.1016/j.compbiomed.2024.108751] [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: 11/26/2023] [Revised: 06/09/2024] [Accepted: 06/10/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Automatic abnormalities detection based on Electrocardiogram (ECG) contributes greatly to early prevention, computer aided diagnosis, and dynamic analysis of cardiovascular diseases. In order to achieve cardiologist-level performance, deep neural networks have been widely utilized to extract abstract feature representations. However, the mechanical stacking of numerous computationally intensive operations makes traditional deep neural networks suffer from inadequate learning, poor interpretability, and high complexity. METHOD To address these limitations, a clinical knowledge-based ECG abnormalities detection model using dual-view CNN-Transformer and external attention mechanism is proposed by mimicking the diagnosis of the clinicians. Considering the clinical knowledge that both the detailed waveform changes within a single heartbeat and the global changes throughout the entire recording have complementary roles in abnormalities detection, we presented a dual-view CNN-Transformer to extract and fuse spatial-temporal features from different views. In addition, the locations of the ECG where abnormalities occur provide more information than other areas. Therefore, two external attention mechanisms are designed and added to the corresponding views to help the network learn efficiently. RESULTS Experiment results on the 9-class dataset show that the proposed model achieves an average F1-score of 0.854±0.01 with a higher interpretability and a lower complexity, outperforming the state-of-the-art model. CONCLUSIONS Combining all these excellent features, this study provides a credible solution for automatic ECG abnormalities detection.
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Affiliation(s)
- Hui Li
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China
| | - Jiyang Han
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China
| | - Honghao Zhang
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Xi Zhang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China
| | - Yingjun Si
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China
| | - Yu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Yu Liu
- Department of Cardiology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing 210008, China
| | - Hui Yang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China.
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2
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Berrahou N, El Alami A, Mesbah A, El Alami R, Berrahou A. Arrhythmia detection in inter-patient ECG signals using entropy rate features and RR intervals with CNN architecture. Comput Methods Biomech Biomed Engin 2024:1-20. [PMID: 39021157 DOI: 10.1080/10255842.2024.2378105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 06/27/2024] [Indexed: 07/20/2024]
Abstract
The classification of inter-patient ECG data for arrhythmia detection using electrocardiogram (ECG) signals presents a significant challenge. Despite the recent surge in deep learning approaches, there remains a noticeable gap in the performance of inter-patient ECG classification. In this study, we introduce an innovative approach for ECG classification in arrhythmia detection by employing a 1D convolutional neural network (CNN) to leverage both morphological and temporal characteristics of cardiac cycles. Through the utilization of 1D-CNN layers, we automatically capture the morphological attributes of ECG data, allowing us to represent the shape of the ECG waveform around the R peaks. Additionally, we incorporate four RR interval features to provide temporal context, and we explore the potential application of entropy rate as a feature extraction technique for ECG signal classification. Consequently, the classification layers benefit from the combination of both temporal and learned features, leading to the achievement of the final arrhythmia classification. We validate our approach using the MIT-BIH arrhythmia dataset, employing both intra-patient and inter-patient paradigms for model training and testing. The model's generalization ability is assessed by evaluating it on the INCART dataset. The model attains average accuracy rates of 99.13% and 99.17% for 2-fold and 5-fold cross-validation, respectively, in intra-patient classification with five classes. In inter-patient classification with three and five classes, the model achieves average accuracies of 98.73% and 97.91%, respectively. For the INCART dataset, the model achieves an average accuracy of 98.20% for three classes. The experimental outcomes demonstrate the superiority of the proposed model compared to state-of-the-art models in recognizing arrhythmias. Thus, the proposed model exhibits enhanced generalization and the potential to serve as an effective solution for recognizing arrhythmias in real-world datasets characterized by class imbalances in practical applications.
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Affiliation(s)
- Nadia Berrahou
- Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Abdelmajid El Alami
- Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | | | - Rachid El Alami
- Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
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Benchaira K, Bitam S. Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach. Biomed Phys Eng Express 2024; 10:045010. [PMID: 38640904 DOI: 10.1088/2057-1976/ad40b0] [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] [Accepted: 04/19/2024] [Indexed: 04/21/2024]
Abstract
Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers. Our models demonstrate exceptional performance, particularly when utilizing embeddings from SCNNs trained on diverse datasets. This underscores the importance of data diversity in improving classifier discrimination. Notably, Multilayer Perceptrons (MLPs) stand out for their ability to balance computational efficiency with strong performance, achieving test F1-scores of 0.94 and 1.00 in multi-class and binary tasks on the CinC2017 dataset, and 0.85 and 0.99 on the CPSC2018 dataset. Our approach consistently outperforms existing methods, setting new benchmarks in ECG classification. The synergy between deep learning-based feature extraction and traditional machine learning through transfer learning offers a robust, efficient, and adaptable strategy for ECG classification, addressing a critical research gap and laying the groundwork for future advancements in this crucial healthcare field.
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Affiliation(s)
- Khadidja Benchaira
- Department of Computer Science, University of Biskra, BP 145 RP, 07000, Algeria
| | - Salim Bitam
- Department of Computer Science, University of Biskra, BP 145 RP, 07000, Algeria
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4
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Zubair M, Woo S, Lim S, Kim D. Deep Representation Learning With Sample Generation and Augmented Attention Module for Imbalanced ECG Classification. IEEE J Biomed Health Inform 2024; 28:2461-2472. [PMID: 37851553 DOI: 10.1109/jbhi.2023.3325540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
Developing an efficient heartbeat monitoring system has become a focal point in numerous healthcare applications. Specifically, in the last few years, heartbeat classification for arrhythmia detection has gained considerable interest from researchers. This paper presents a novel deep representation learning method for the efficient detection of arrhythmic beats. To mitigate the issues associated with the imbalanced data distribution, a novel re-sampling strategy is introduced. Unlike the existing oversampling methods, the proposed technique transforms majority-class samples into minority-class samples with a novel translation loss function. This approach assists the model in learning a more generalized representation of crucially important minority class samples. Moreover, by exploiting an auxiliary feature, an augmented attention module is designed that focuses on the most relevant and target-specific information. We adopted an inter-patient classification paradigm to evaluate the proposed method. The experimental results of this study on the MIT-BIH arrhythmia database clearly indicate that the proposed model with augmented attention mechanism and over-sampling strategy significantly learns a balanced deep representation and improves the classification performance of vital heartbeats.
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5
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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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Xie J, Stavrakis S, Yao B. Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet. Front Physiol 2024; 15:1362185. [PMID: 38655032 PMCID: PMC11035782 DOI: 10.3389/fphys.2024.1362185] [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: 12/27/2023] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. Electrocardiography (ECG), which involves acquiring bioelectrical signals from the body surface to reflect heart activity, is a standard procedure for detecting AF. However, the occurrence of AF is often intermittent, costing a significant amount of time and effort from medical doctors to identify AF episodes. Moreover, human error is inevitable, as even experienced medical professionals can overlook or misinterpret subtle signs of AF. As such, it is of critical importance to develop an advanced analytical model that can automatically interpret ECG signals and provide decision support for AF diagnostics. Methods: In this paper, we propose an innovative deep-learning method for automated AF identification using single-lead ECGs. We first extract time-frequency features from ECG signals using continuous wavelet transform (CWT). Second, the convolutional neural networks enhanced with residual learning (ReNet) are employed as the functional approximator to interpret the time-frequency features extracted by CWT. Third, we propose to incorporate a multi-branching structure into the ResNet to address the issue of class imbalance, where normal ECGs significantly outnumber instances of AF in ECG datasets. Results and Discussion: We evaluate the proposed Multi-branching Resnet with CWT (CWT-MB-Resnet) with two ECG datasets, i.e., PhysioNet/CinC challenge 2017 and ECGs obtained from the University of Oklahoma Health Sciences Center (OUHSC). The proposed CWT-MB-Resnet demonstrates robust prediction performance, achieving an F1 score of 0.8865 for the PhysioNet dataset and 0.7369 for the OUHSC dataset. The experimental results signify the model's superior capability in balancing precision and recall, which is a desired attribute for ensuring reliable medical diagnoses.
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Affiliation(s)
- Jianxin Xie
- School of Data Science, University of Virginia, Charlottesville, VA, United States
| | - Stavros Stavrakis
- Health Sciences Center, University of Oklahoma, Oklahoma City, OK, United States
| | - Bing Yao
- Department of Industrial and Systems Engineering, University of Tennessee at Knoxville, Knoxville, TN, United States
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Jha CK. Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach. Comput Methods Biomech Biomed Engin 2024:1-16. [PMID: 38566498 DOI: 10.1080/10255842.2024.2332942] [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: 04/20/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac anomalies seeks early detection of arrhythmias using automated classification techniques. In the past, numerous automated arrhythmia detection techniques have been developed that are based on electrocardiogram (ECG) signal analysis. Focusing on the prospective research in this field, this article reports a comprehensive review of existing techniques that are obtained using search engines such as IEEE explore, Google scholar and science direct. Based on the review, the existing techniques are broadly categorized into two types: machine-learning and deep-learning-based techniques. In this study, it is noticed that the performance of the machine-learning-based arrhythmia detection techniques depend on pre-processing of ECG signal, R-peaks detection, features extraction and classification tools while the deep-learning-based techniques do not require the features extraction step. Generally, the existing techniques utilize Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database to evaluate the classification performance. The classification performance of automated techniques also depends on ECG data used for training and testing of the classifier. It is expected that the performance should be evaluated using a variety of ECG signals including the cases of inter-patient and intra-patient paradigm. The existing techniques also require to deal with the class-imbalance problem. In addition to this, a specific partition-ratio between training and testing datasets should be maintained for fair comparison of performance of different techniques.
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Affiliation(s)
- Chandan Kumar Jha
- Department of Electronics & Communication Engineering, Indian Institute of Information Technology Bhagalpur, India
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8
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Lin M, Hong Y, Hong S, Zhang S. Discrete Wavelet Transform based ECG classification using gcForest: A deep ensemble method. Technol Health Care 2024; 32:95-105. [PMID: 38759040 PMCID: PMC11191494 DOI: 10.3233/thc-248008] [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] [Indexed: 05/19/2024]
Abstract
BACKGROUND Cardiovascular diseases (CVDs) are the leading global cause of mortality, necessitating advanced diagnostic tools for early detection. The electrocardiogram (ECG) is pivotal in diagnosing cardiac abnormalities due to its non-invasive nature. OBJECTIVE This study aims to propose a novel approach for ECG signal classification, addressing the challenges posed by the complexity of ECG signals associated with various diseases. METHODS Our method integrates Discrete Wavelet Transform (DWT) for feature extraction, capturing salient features of cardiovascular diseases. Subsequently, the gcForest model is employed for efficient classification. The approach is tested on the MIT-BIH Arrhythmia Database. RESULTS The proposed method demonstrates promising results on the MIT-BIH Arrhythmia Database, achieving a test accuracy of 98.55%, recall of 98.48%, precision of 98.44%, and an F1 score of 98.46%. Additionally, the model exhibits robustness and low sensitivity to hyper-parameters. CONCLUSION The combined use of DWT and the gcForest model proves effective in ECG signal classification, showcasing high accuracy and reliability. This approach holds potential for improving early detection of cardiovascular diseases, contributing to enhanced cardiac healthcare.
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Affiliation(s)
- Mingfeng Lin
- Department of General Surgery, Zhongshan Hospital of Xiamen University, Xiamen, Fujian, China
- School of Informatics, Xiamen University, Xiamen, Fujian, China
| | - Yuanzhen Hong
- Department of General Surgery, Zhongshan Hospital of Xiamen University, Xiamen, Fujian, China
- Hepatology Department’s Three Wards, Xiamen Hospital, Beijing University of Chinese Medicine, Xiamen, Fujian, China
| | - Shichai Hong
- Department of Vascular Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian, China
| | - Suzhen Zhang
- Department of General Surgery, Zhongshan Hospital of Xiamen University, Xiamen, Fujian, China
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Dózsa T, Deuschle F, Cornelis B, Kovács P. Variable Projection Support Vector Machines and Some Applications Using Adaptive Hermite Expansions. Int J Neural Syst 2024; 34:2450004. [PMID: 38073547 DOI: 10.1142/s0129065724500047] [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] [Indexed: 12/28/2023]
Abstract
In this paper, we develop the so-called variable projection support vector machine (VP-SVM) algorithm that is a generalization of the classical SVM. In fact, the VP block serves as an automatic feature extractor to the SVM, which are trained simultaneously. We consider the primal form of the arising optimization task and investigate the use of nonlinear kernels. We show that by choosing the so-called adaptive Hermite function system as the basis of the orthogonal projections in our classification scheme, several real-world signal processing problems can be successfully solved. In particular, we test the effectiveness of our method in two case studies corresponding to anomaly detection. First, we consider the detection of abnormal peaks in accelerometer data caused by sensor malfunction. Then, we show that the proposed classification algorithm can be used to detect abnormalities in ECG data. Our experiments show that the proposed method produces comparable results to the state-of-the-art while retaining desired properties of SVM classification such as light weight architecture and interpretability. We implement the proposed method on a microcontroller and demonstrate its ability to be used for real-time applications. To further minimize computational cost, discrete orthogonal adaptive Hermite functions are introduced for the first time.
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Affiliation(s)
- Tamás Dózsa
- Department of Numerical Analysis, HUN-REN Institute for Computer Science and Control, Eötvös Loránd University, Budapest H-1111, Hungary
| | - Federico Deuschle
- Siemens Digital Industries Software, 68 Interleuvenlaan KU Leuven, Department of Mechanical Engineering, Leuven B-3001, Belgium
| | - Bram Cornelis
- Siemens Digital Industries Software, 68 Interleuvenlaan KU Leuven, Department of Mechanical Engineering, Leuven B-3001, Belgium
| | - Péter Kovács
- Department of Numerical Analysis, Eötvös Loránd University, Pázmány Péter sétány 1/C Budapest 1117, Hungary
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Tang X, Renteria-Pinon M, Tang W. Second-Order Level-Crossing Sampling Analog to Digital Converter for Electrocardiogram Delineation and Premature Ventricular Contraction Detection. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:1342-1354. [PMID: 37463086 DOI: 10.1109/tbcas.2023.3296529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
This article presents an electrocardiogram (ECG) delineation and arrhythmia heartbeat detection system using a novel second-order level-crossing sampling analog to digital converter (ADC) for real-time data compression and feature extraction. The proposed system consists of the front-end integrated circuit of the data converter, the delineation algorithm, and the arrhythmia detection algorithm. Compared with conventional level-sampling ADCs, the proposed circuit updates tracking thresholds using linear extrapolation, which forms a second-order level-crossing sampling ADC that has sloped sampling levels. The computing is done digitally and is implemented by modifying the digital control logic of a conventional Successive-approximation-register (SAR) ADC. The system separates the sampling and quantization processes and only selects the turning points in the input waveform for quantization. The output of the proposed data converter consists of both the digital value of the selected sampling points and the timestamp between the selected sampling points. The main advantages are data savings for the data converter and the following digital signal processing or communication circuits, which are ideal for low-power sensors. The test chip was fabricated using a 180 nm CMOS process. When sensing sparse signals such as ECG signals the proposed ADC achieves a compression factor of 8.33. The delineation algorithm uses a triangle filter method to locate the fiducial points and measures the intervals, slopes, and morphology of the QRS complex and the P/T waves. Those extracted features are then used in the arrhythmia heartbeat detection algorithm to identify Premature Ventricular Contraction (PVC). The overall performance of the system is evaluated using the MIT-BIH database and the QT database, which is also compared with the recently reported systems. The accuracy, sensitivity, specificity, PPV, and F1 score are 97.3%, 89.6%, 97.8%, 73.3%, and 0.81 for detecting PVC.
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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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Zhou C, Li X, Feng F, Zhang J, Lyu H, Wu W, Tang X, Luo B, Li D, Xiang W, Yao D. Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination. Front Physiol 2023; 14:1247587. [PMID: 37841320 PMCID: PMC10569428 DOI: 10.3389/fphys.2023.1247587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/07/2023] [Indexed: 10/17/2023] Open
Abstract
Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement. Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method's performance is further evaluated. Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm. Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples.
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Affiliation(s)
- Chenchen Zhou
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
- Guangxi Key Laboratory of Digital Infrastructure, Guangxi Information Center, Nanning, China
| | - Xiangkui Li
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Fan Feng
- Guangxi Key Laboratory of Digital Infrastructure, Guangxi Information Center, Nanning, China
| | - Jian Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - He Lyu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
| | - Weixuan Wu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
| | - Xuezhi Tang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
| | - Bin Luo
- Sichuan Huhui Software Co., Ltd., Mianyang, China
| | - Dong Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Wei Xiang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
| | - Dengju Yao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
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13
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Qi M, Shao H, Shi N, Wang G, Lv Y. Arrhythmia classification detection based on multiple electrocardiograms databases. PLoS One 2023; 18:e0290995. [PMID: 37756278 PMCID: PMC10529562 DOI: 10.1371/journal.pone.0290995] [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: 01/17/2023] [Accepted: 08/20/2023] [Indexed: 09/29/2023] Open
Abstract
According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.
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Affiliation(s)
- Meng Qi
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Hongxiang Shao
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Nianfeng Shi
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Guoqiang Wang
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Yifei Lv
- School of Computer Science and Engineering Department, Tianjin University of Technology, Tianjin, China
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14
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Su S, Zhu Z, Wan S, Sheng F, Xiong T, Shen S, Hou Y, Liu C, Li Y, Sun X, Huang J. An ECG Signal Acquisition and Analysis System Based on Machine Learning with Model Fusion. SENSORS (BASEL, SWITZERLAND) 2023; 23:7643. [PMID: 37688099 PMCID: PMC10490810 DOI: 10.3390/s23177643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
Recently, cardiovascular disease has become the leading cause of death worldwide. Abnormal heart rate signals are an important indicator of cardiovascular disease. At present, the ECG signal acquisition instruments on the market are not portable and manual analysis is applied in data processing, which cannot address the above problems. To solve these problems, this study proposes an ECG acquisition and analysis system based on machine learning. The ECG analysis system responsible for ECG signal classification includes two parts: data preprocessing and machine learning models. Multiple types of models were built for overall classification, and model fusion was conducted. Firstly, traditional models such as logistic regression, support vector machines, and XGBoost were employed, along with feature engineering that primarily included morphological features and wavelet coefficient features. Subsequently, deep learning models, including convolutional neural networks and long short-term memory networks, were introduced and utilized for model fusion classification. The system's classification accuracy for ECG signals reached 99.13%. Future work will focus on optimizing the model and developing a more portable instrument that can be utilized in the field.
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Affiliation(s)
- Shi Su
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
- Innovative Research Laboratory of Nanjing Xi-Jing Advanced Materials Technology Ltd., Nanjing 211101, China
- SEU-FEI Nano-Pico Center, Key Lab of MEMS of Ministry of Education, Collaborative Innovation, Center for Micro/Nano Fabrication, Device and System, Southeast University, Nanjing 210096, China
| | - Zhihong Zhu
- SEU-FEI Nano-Pico Center, Key Lab of MEMS of Ministry of Education, Collaborative Innovation, Center for Micro/Nano Fabrication, Device and System, Southeast University, Nanjing 210096, China
| | - Shu Wan
- SEU-FEI Nano-Pico Center, Key Lab of MEMS of Ministry of Education, Collaborative Innovation, Center for Micro/Nano Fabrication, Device and System, Southeast University, Nanjing 210096, China
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, School of Optoelectronics Engineering, Chongqing University, Chongqing 400044, China
| | - Fangqing Sheng
- School of Economics and Management, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Tianyi Xiong
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Shanshan Shen
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Yu Hou
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Cuihong Liu
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Yijin Li
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Xiaolin Sun
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
| | - Jie Huang
- School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
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15
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Boda S, Mahadevappa M, Kumar Dutta P. An automated patient-specific ECG beat classification using LSTM-based recurrent neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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16
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Lyu H, Li X, Zhang J, Zhou C, Tang X, Xu F, Yang Y, Huang Q, Xiang W, Li D. Automated inter-patient arrhythmia classification with dual attention neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107560. [PMID: 37116424 DOI: 10.1016/j.cmpb.2023.107560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND AND OBJECTIVES Arrhythmia classification based on electrocardiograms (ECG) can enhance clinical diagnostic efficiency. However, due to the significant differences in the number of different categories of heartbeats, the performance of classes with fewer samples in arrhythmia classification have not met expectations under the inter-patient paradigm. This paper aims to mitigate the adverse effects of category imbalance and improve arrhythmia classification performance. METHODS We constructed a novel dual attention hybrid network (DA-Net) for arrhythmia classification under sample imbalance, based on modified convolutional networks with channel attention (MCC-Net) and sequence-to-sequence network with global attention (Seq2Seq). The refined local features of the input heartbeat are first extracted by MCC-Net and then sent to Seq2Seq for further feature fusion. By applying local and global attention in the feature extraction and fusion parts, respectively, the method fully fuses low-level feature details and high-level context information and enhances the ability to extract discriminative features. RESULTS Based on the MIT-BIH arrhythmia database, under the inter-patient paradigm without any data augmentation methods, the proposed method achieved 99.98% accuracy (ACC) for five categories. The various performance indicators are as follows: Class N: sensitivity (SEN) = 99.96%, specificity (SPEC) = 99.93%, positive predictive value (PPV) = 99.99%; Class S: SEN = 99.67%, SPEC = 99.98%, PPV = 99.56%; Class V: SEN = 100%, SPEC = 99.99%, PPV = 99.91%; Class F: SEN = 100%, PPV = 99.98%, SPEC = 97.17%. In further experiments simulating extreme cases, the model still achieved ACC of 99.54% and 98.91% in the three-category and five-category categories when the training sample size was much smaller than the test sample. CONCLUSIONS Without any data augmentation methods, the proposed model not only alleviates the negative impact of class imbalance and achieves excellent performance in all categories but also provides a new approach for dealing with class imbalance in arrhythmia classification. Additionally, our method demonstrates potential in conditions with fewer samples.
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Affiliation(s)
- He Lyu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Xiangkui Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University. 37 Guoxue Alley, Chengdu 610041. China
| | - Jian Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University. 37 Guoxue Alley, Chengdu 610041. China
| | - Chenchen Zhou
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Xuezhi Tang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Fanxin Xu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Ye Yang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Qinzhen Huang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China.
| | - Wei Xiang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China.
| | - Dong Li
- Division of Hospital Medicine, Emory School of Medicine, 201 Dowman Dr, Atlanta, GA 30322, USA
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17
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Huang Y, Li H, Yu X. A novel time representation input based on deep learning for ECG classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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18
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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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19
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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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20
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Hassaballah M, Wazery YM, Ibrahim IE, Farag A. ECG Heartbeat Classification Using Machine Learning and Metaheuristic Optimization for Smart Healthcare Systems. Bioengineering (Basel) 2023; 10:bioengineering10040429. [PMID: 37106616 PMCID: PMC10135930 DOI: 10.3390/bioengineering10040429] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 03/31/2023] Open
Abstract
Early diagnosis and classification of arrhythmia from an electrocardiogram (ECG) plays a significant role in smart healthcare systems for the health monitoring of individuals with cardiovascular diseases. Unfortunately, the nonlinearity and low amplitude of ECG recordings make the classification process difficult. Thus, the performance of most traditional machine learning (ML) classifiers is questionable, as the interrelationship between the learning parameters is not well modeled, especially for data features with high dimensions. To address the limitations of ML classifiers, this paper introduces an automatic arrhythmia classification approach based on the integration of a recent metaheuristic optimization (MHO) algorithm and ML classifiers. The role of the MHO is to optimize the search parameters of the classifiers. The approach consists of three steps: the preprocessing of the ECG signal, the extraction of the features, and the classification. The learning parameters of four supervised ML classifiers were utilized for the classification task; support vector machine (SVM), k-nearest neighbors (kNNs), gradient boosting decision tree (GBDT), and random forest (RF) were optimized using the MHO algorithm. To validate the advantage of the proposed approach, several experiments were conducted on three common databases, including the Massachusetts Institute of Technology (MIT-BIH), the European Society of Cardiology ST-T (EDB), and the St. Petersburg Institute of Cardiological Techniques 12-lead Arrhythmia (INCART). The obtained results showed that the performance of all the tested classifiers were significantly improved after integrating the MHO algorithm, with the average ECG arrhythmia classification accuracy reaching 99.92% and a sensitivity of 99.81%, outperforming the state-of the-art methods.
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21
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Pham BT, Le PT, Tai TC, Hsu YC, Li YH, Wang JC. Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction. SENSORS (BASEL, SWITZERLAND) 2023; 23:2993. [PMID: 36991703 PMCID: PMC10051525 DOI: 10.3390/s23062993] [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: 01/14/2023] [Revised: 02/23/2023] [Accepted: 02/24/2023] [Indexed: 06/19/2023]
Abstract
An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.
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Affiliation(s)
- Bach-Tung Pham
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
| | - Phuong Thi Le
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 320317, Taiwan
| | - Tzu-Chiang Tai
- Department of Computer Science and Information Engineering, Providence University, Taichung City 43301, Taiwan
| | - Yi-Chiung Hsu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 320317, Taiwan
| | - Yung-Hui Li
- AI Research Center, Hon Hai Research Institute, New Taipei City 236, Taiwan
| | - Jia-Ching Wang
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
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22
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Islam MS, Hasan KF, Sultana S, Uddin S, Lio' P, Quinn JMW, Moni MA. HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN. Neural Netw 2023; 162:271-287. [PMID: 36921434 DOI: 10.1016/j.neunet.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 09/21/2022] [Accepted: 03/02/2023] [Indexed: 03/07/2023]
Abstract
Deep learning-based models have achieved significant success in detecting cardiac arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the performance of such models, we have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60%, F1 score of 98.21%, a precision of 97.66%, and recall of 99.60% using MIT-BIH generated ECG. In addition, this approach significantly reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.
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Affiliation(s)
- Md Shofiqul Islam
- Faculty of Computing, Universiti Malaysia Pahang, Gambang 26300, Kuantan, Pahang, Malaysia; IBM Centre of Excellence, Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang (UMP), Lebuhraya Tun Razak, Gambang 26300, Kuantan, Pahang, Malaysia
| | - Khondokar Fida Hasan
- School of Computer Science, Queensland University of Technology (QUT), 2 George Street, Brisbane 4000, Australia
| | - Sunjida Sultana
- Department of Computer Science and Engineering, Islamic University, Kushtia 7600, Bangladesh
| | - Shahadat Uddin
- School of Project Management, Faculty of Engineering, The University of Sydney, Sydney, Australia
| | - Pietro Lio'
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Julian M W Quinn
- Bone Research Group, The Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Mohammad Ali Moni
- Artificial Intelligence & Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland St Lucia, QLD 4072, Australia.
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23
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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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24
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Xia Y, Xu Y, Chen P, Zhang J, Zhang Y. Generative adversarial network with transformer generator for boosting ECG classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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25
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A lightweight 2-D CNN model with dual attention mechanism for heartbeat classification. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04303-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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26
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Sun F, Ni Y, Luo Y, Sun H. ECG Classification Based on Wasserstein Scalar Curvature. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1450. [PMID: 37420470 DOI: 10.3390/e24101450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/04/2022] [Accepted: 10/06/2022] [Indexed: 07/09/2023]
Abstract
Electrocardiograms (ECG) analysis is one of the most important ways to diagnose heart disease. This paper proposes an efficient ECG classification method based on Wasserstein scalar curvature to comprehend the connection between heart disease and the mathematical characteristics of ECG. The newly proposed method converts an ECG into a point cloud on the family of Gaussian distribution, where the pathological characteristics of ECG will be extracted by the Wasserstein geometric structure of the statistical manifold. Technically, this paper defines the histogram dispersion of Wasserstein scalar curvature, which can accurately describe the divergence between different heart diseases. By combining medical experience with mathematical ideas from geometry and data science, this paper provides a feasible algorithm for the new method, and the theoretical analysis of the algorithm is carried out. Digital experiments on the classical database with large samples show the new algorithm's accuracy and efficiency when dealing with the classification of heart disease.
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Affiliation(s)
- Fupeng Sun
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China
| | - Yin Ni
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China
| | - Yihao Luo
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China
| | - Huafei Sun
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China
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27
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Liu J, Li Z, Fan X, Hu X, Yan J, Li B, Xia Q, Zhu J, Wu Y. CRT-Net: A generalized and scalable framework for the computer-aided diagnosis of Electrocardiogram signals. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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28
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Toma TI, Choi S. A Parallel Cross Convolutional Recurrent Neural Network for Automatic Imbalanced ECG Arrhythmia Detection with Continuous Wavelet Transform. SENSORS (BASEL, SWITZERLAND) 2022; 22:7396. [PMID: 36236496 PMCID: PMC9573388 DOI: 10.3390/s22197396] [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: 08/31/2022] [Revised: 09/19/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
Automatic detection of arrhythmia using electrocardiogram (ECG) and deep learning (DL) is very important to reduce the global death rate from cardiovascular diseases (CVD). Previous studies on automatic arrhythmia detection relied largely on various ECG features and have achieved considerable classification accuracy using DL-based models. However, most previous research has ignored multi-class imbalanced problems in ECG arrhythmia detection. Therefore, it remains a challenge to improve the classification performance of the DL-based models. This paper proposes a novel parallel cross convolutional recurrent neural network in order to improve the arrhythmia detection performance of imbalanced ECG signals. The proposed model incorporates a recurrent neural network and a two-dimensional (2D) convolutional neural network (CNN) and can effectively learn temporal characteristics and rich spatial information of raw ECG signals. Continuous wavelet transform (CWT) is used to transform the ECG signals into a 2D scalogram composed of time-frequency components, and subsequently, the 2D-CNN can learn spatial information from the 2D scalogram. The proposed model is not only efficient in learning features with imbalanced samples but can also significantly improve model convergence with higher accuracy. The overall performance of our proposed model is evaluated based on the MIT-BIH arrhythmia dataset. Detailed analysis of evaluation metrics reveals that the proposed model is very effective in arrhythmia detection and significantly better than the existing hierarchical network models.
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29
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A Deep Neural Network Ensemble Classifier with Focal Loss for Automatic Arrhythmia Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9370517. [PMID: 36118121 PMCID: PMC9481402 DOI: 10.1155/2022/9370517] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/29/2022] [Accepted: 08/09/2022] [Indexed: 11/18/2022]
Abstract
Automated electrocardiogram classification techniques play an important role in assisting physicians in diagnosing arrhythmia. Among these, the automatic classification of single-lead heartbeats has received wider attention due to the urgent need for portable ECG monitoring devices. Although many heartbeat classification studies performed well in intrapatient assessment, they do not perform as well in interpatient assessment. In particular, for supraventricular ectopic heartbeats (S), most models do not classify them well. To solve these challenges, this article provides an automated arrhythmia classification algorithm. There are three key components of the algorithm. First, a new heartbeat segmentation method is used, which improves the algorithm’s capacity to classify S substantially. Second, to overcome the problems created by data imbalance, a combination of traditional sampling and focal loss is applied. Finally, using the interpatient evaluation paradigm, a deep convolutional neural network ensemble classifier is built to perform classification validation. The experimental results show that the overall accuracy of the method is 91.89%, the sensitivity is 85.37%, the positive productivity is 59.51%, and the specificity is 93.15%. In particular, for the supraventricular ectopic heartbeat(s), the method achieved a sensitivity of 80.23%, a positivity of 49.40%, and a specificity of 96.85%, exceeding most existing studies. Even without any manually extracted features or heartbeat preprocessing, the technique achieved high classification performance in the interpatient assessment paradigm.
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30
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A novel technique for the detection of myocardial dysfunction using ECG signals based on CEEMD, DWT, PSR and neural networks. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10262-w] [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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31
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Sinha N, Kumar Tripathy R, Das A. ECG beat classification based on discriminative multilevel feature analysis and deep learning approach. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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32
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Shannon entropy Morlet wavelet Transform (SEMWT) and Kernel Weight Convolutional Neural Network (KWCNN) classifier for arrhythmia in electrocardiogram recordings. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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33
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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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An efficient neural network-based method for patient-specific information involved arrhythmia detection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhang H, Liu C, Tang F, Li M, Zhang D, Xia L, Zhao N, Li S, Crozier S, Xu W, Liu F. Cardiac Arrhythmia classification based on 3D recurrence plot analysis and deep learning. Front Physiol 2022; 13:956320. [PMID: 35936913 PMCID: PMC9352947 DOI: 10.3389/fphys.2022.956320] [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: 05/30/2022] [Accepted: 06/27/2022] [Indexed: 11/29/2022] Open
Abstract
Artificial intelligence (AI) aided cardiac arrhythmia (CA) classification has been an emerging research topic. Existing AI-based classification methods commonly analyze electrocardiogram (ECG) signals in lower dimensions, using one-dimensional (1D) temporal signals or two-dimensional (2D) images, which, however, may have limited capability in characterizing lead-wise spatiotemporal correlations, which are critical to the classification accuracy. In addition, existing methods mostly assume that the ECG data are linear temporal signals. This assumption may not accurately represent the nonlinear, nonstationary nature of the cardiac electrophysiological process. In this work, we have developed a three-dimensional (3D) recurrence plot (RP)-based deep learning algorithm to explore the nonlinear recurrent features of ECG and Vectorcardiography (VCG) signals, aiming to improve the arrhythmia classification performance. The 3D ECG/VCG images are generated from standard 12 lead ECG and 3 lead VCG signals for neural network training, validation, and testing. The superiority and effectiveness of the proposed method are validated by various experiments. Based on the PTB-XL dataset, the proposed method achieved an average F1 score of 0.9254 for the 3D ECG-based case and 0.9350 for the 3D VCG-based case. In contrast, recently published 1D and 2D ECG-based CA classification methods yielded lower average F1 scores of 0.843 and 0.9015, respectively. Thus, the improved performance and visual interpretability make the proposed 3D RP-based method appealing for practical CA classification.
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Affiliation(s)
- Hua Zhang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Fangfang Tang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Mingyan Li
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Dongxia Zhang
- Zhejiang Provincial Centre for Disease Control and Prevention CN, Hangzhou, China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Nan Zhao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Sheng Li
- The College of Science, Xijing University, Xi’an, China
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Wenlong Xu
- Department of Biomedical Engineering, China Jiliang University, Hangzhou, China
- *Correspondence: Wenlong Xu, ; Feng Liu,
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Wenlong Xu, ; Feng Liu,
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36
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Karri M, Annavarapu CSR, Pedapenki KK. A Real-Time Cardiac Arrhythmia Classification Using Hybrid Combination of Delta Modulation, 1D-CNN and Blended LSTM. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10949-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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37
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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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38
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Class-specific weighted broad learning system for imbalanced heartbeat classification. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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39
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Ojha MK, Wadhwani S, Wadhwani AK, Shukla A. Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier. Phys Eng Sci Med 2022; 45:665-674. [PMID: 35304901 DOI: 10.1007/s13246-022-01119-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 03/09/2022] [Indexed: 12/29/2022]
Abstract
Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are harmful to the heart and can suddenly become life-threatening. The electrocardiogram (ECG) is an important non-invasive tool in cardiology for the diagnosis of arrhythmias. This work proposes a computer-aided diagnosis (CAD) system to automatically classify different types of arrhythmias from ECG signals. First, the auto-encoder convolutional network (ACN) model is used, which is based on a one-dimensional convolutional neural network (1D-CNN) that automatically learns the best features from the raw ECG signals. After that, the support vector machine (SVM) classifier is applied to the features learned by the ACN model to improve the detection of arrhythmic beats. This classifier detects four different types of arrhythmias, namely the left bundle branch block (LBBB), right bundle branch block (RBBB), paced beat (PB), and premature ventricular contractions (PVC), along with the normal sinus rhythms (NSR). Among these arrhythmias, PVC is particularly a dangerous type of heartbeat in ECG signals. The performance of the model is measured in terms of accuracy, sensitivity, and precision using a tenfold cross-validation strategy on the MIT-BIH arrhythmia database. The obtained overall accuracy of the SVM classifier was 98.84%. The result of this model is portrayed as a better performance than in other literary works. Thus, this approach may also help in further clinical studies of cardiac cases.
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Affiliation(s)
- Manoj Kumar Ojha
- Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India.
| | - Sulochna Wadhwani
- Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India
| | | | - Anupam Shukla
- Indian Institute of Information Technology, Pune, Maharashtra, India
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40
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Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks. SENSORS 2022; 22:s22114075. [PMID: 35684694 PMCID: PMC9185309 DOI: 10.3390/s22114075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022]
Abstract
Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to their vital role in the diagnosis of cardiac abnormalities. Despite this interest, deep feature representation for ECG is still challenging and intriguing due to the inter-patient variability of the ECG’s morphological characteristics. The aim of this study was to learn a balanced deep feature representation that incorporates both the short-term and long-term morphological characteristics of ECG beats. For efficient feature extraction, we designed a temporal transition module that uses convolutional layers with different kernel sizes to capture a wide range of morphological patterns. Imbalanced data are a key issue in developing an efficient and generalized model for arrhythmia detection as they cause over-fitting to minority class samples (abnormal beats) of primary interest. To mitigate the imbalanced data issue, we proposed a novel, cost-sensitive loss function that ensures a balanced deep representation of class samples by assigning effective weights to each class. The cost-sensitive loss function dynamically alters class weights for every batch based on class distribution and model performance. The proposed method acquired an overall accuracy of 99.81% for intra-patient classification and 96.36% for the inter-patient classification of heartbeats. The experimental results reveal that the proposed approach learned a balanced representation of ECG beats by mitigating the issue of imbalanced data and achieved an improved classification performance as compared to other studies.
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41
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Song G, Zhang J, Mao D, Chen G, Pang C. A Multimodel Fusion Method for Cardiovascular Disease Detection Using ECG. Emerg Med Int 2022; 2022:3561147. [PMID: 35615106 PMCID: PMC9126725 DOI: 10.1155/2022/3561147] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/13/2022] [Accepted: 04/25/2022] [Indexed: 11/25/2022] Open
Abstract
Objective. Electrocardiogram (ECG) is an important diagnostic tool that has been the subject of much research in recent years. Owing to a lack of well-labeled ECG record databases, most of this work has focused on heartbeat arrhythmia detection based on ECG signal quality. Approach. A record quality filter was designed to judge ECG signal quality, and a random forest method, a multilayer perceptron, and a residual neural network (RESNET)-based convolutional neural network were implemented to provide baselines for ECG record classification according to three different principles. A new multimodel method was constructed by fusing the random forest and RESNET approaches. Main Results. Owing to its ability to combine discriminative human-crafted features with RESNET deep features, the proposed new method showed over 88% classification accuracy and yielded the best results in comparison with alternative methods. Significance. A new multimodel fusion method was presented for abnormal cardiovascular detection based on ECG data. The experimental results show that separable convolution and multiscale convolution are vital for ECG record classification and are effective for use with one-dimensional ECG sequences.
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Affiliation(s)
- Guanghui Song
- School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315100, Zhejiang, China
| | - Jiajian Zhang
- School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315100, Zhejiang, China
| | - Dandan Mao
- Department of Electrocardiogram, Ningbo Hospital of Traditional Chinese Medicine, Ningbo 315100, Zhejiang, China
| | - Genlang Chen
- School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315100, Zhejiang, China
| | - Chaoyi Pang
- School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315100, Zhejiang, China
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42
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Srivastava A, Pratiher S, Alam S, Hari A, Banerjee N, Ghosh N, Patra A. A deep residual inception network with channel attention modules for multi-label cardiac abnormality detection from reduced-lead ECG. Physiol Meas 2022; 43. [PMID: 35550571 DOI: 10.1088/1361-6579/ac6f40] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 05/12/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Most arrhythmias due to cardiovascular diseases alter the electrical activity, resulting in morphological alterations in electrocardiogram (ECG) recordings. ECG acquisition is a low-cost, non-invasive process and is commonly used for continuous monitoring as a diagnostic tool for cardiac abnormality identification. Our objective is to diagnose twenty-nine cardiac abnormalities and sinus rhythm using varied lead ECG signals. APPROACH This work proposes a deep residual inception network with channel attention mechanism (RINCA) for twenty-nine cardiac arrhythmia classification (CAC) along with normal ECG from multi-label ECG signal with different lead combinations. The RINCA architecture employing the Inception-based convolutional neural network backbone uses residual skip connections with the channel attention mechanism. The Inception model facilitates efficient computation and prevents overfitting while exploring deeper networks through dimensionality reduction and stacked 1-dimensional convolutions. The residual skip connections alleviate the vanishing gradient problem. The attention modules selectively leverage the temporally significant segments in a sequence and predominant channels for multi-lead ECG signals, contributing to the decision-making. MAIN RESULTS Exhaustive experimental evaluation on the large-scale 'PhysioNet/Computing in Cardiology Challenge (2021)' dataset demonstrates RINCA efficacy. On the hidden test data set, RINCA achieves the challenge metric score of 0.55, 0.51, 0.53, 0.51, and 0.53 (ranked 2nd, 5th, 4th, 5th and 4th) for the twelve-lead, six-lead, four-lead, three-lead, and two-lead combination cases, respectively. SIGNIFICANCE The proposed RINCA model is more robust against varied sampling frequency, recording time, and data with heterogeneous demographics than the existing art. The explainability analysis shows RINCA potential in clinical interpretations.
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Affiliation(s)
- Apoorva Srivastava
- ELECTRICAL ENGINEERING, Indian Institute of Technology Kharagpur, Department of Electrical Engineering, IIT Kharagpur, Kharagpur, West Bengal 721302, Kharagpur, 721302, INDIA
| | - Sawon Pratiher
- ELECTRICAL ENGINEERING, Indian Institute of Technology Kharagpur, Department of Electrical Engineering, IIT Kharagpur, Kharagpur, West Bengal 721302, Kharagpur, 721302, INDIA
| | - Sazedul Alam
- University of Maryland Baltimore County, University of Maryland, Baltimore County, Baltimore, MD 21250 USA., Baltimore, Maryland, 21250-0001, UNITED STATES
| | - Ajith Hari
- ELECTRICAL ENGINEERING, Indian Institute of Technology Kharagpur, Department of Electrical Engineering, IIT Kharagpur, Kharagpur, West Bengal 721302, Kharagpur, 721302, INDIA
| | - Nilanjan Banerjee
- University of Maryland Baltimore County, University of Maryland, Baltimore County, Baltimore, MD 21250 USA., Baltimore, Maryland, 21250-0001, UNITED STATES
| | - Nirmalya Ghosh
- ELECTRICAL ENGINEERING, Indian Institute of Technology Kharagpur, Department of Electrical Engineering, IIT Kharagpur, Kharagpur, West Bengal 721302, Kharagpur, West Bengal, 721302, INDIA
| | - Amit Patra
- ELECTRICAL ENGINEERING, Indian Institute of Technology Kharagpur, Department of Electrical Engineering, IIT Kharagpur, Kharagpur, West Bengal 721302, Kharagpur, West Bengal, 721302, INDIA
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43
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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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44
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Gholami M, Maleki M, Amirkhani S, Chaibakhsh A. Nonlinear model-based cardiac arrhythmia diagnosis using the optimization-based inverse problem solution. Biomed Eng Lett 2022; 12:205-215. [PMID: 35529347 PMCID: PMC9046521 DOI: 10.1007/s13534-022-00223-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 02/16/2022] [Accepted: 02/19/2022] [Indexed: 10/18/2022] Open
Abstract
This study investigates a nonlinear model-based feature extraction approach for the accurate classification of four types of heartbeats. The features are the morphological parameters of ECG signal derived from the nonlinear ECG model using an optimization-based inverse problem solution. In the model-based methods, high feature extraction time is a crucial issue. In order to reduce the feature extraction time, a new structure was employed in the optimization algorithms. Using the proposed structure has considerably increased the speed of feature extraction. In the following, the effectiveness of two types of optimization methods (genetic algorithm and particle swarm optimization) and the McSharry ECG model has been studied and compared in terms of speed and accuracy of diagnosis. In the classification section, the adaptive neuro-fuzzy inference system and fuzzy c-mean clustering methods, along with the principal component analysis data reduction method, have been utilized. The obtained results reveal that using an adaptive neuro-fuzzy inference system with data obtained from particle swarm optimization will have the shortest process time and the best diagnosis, with a mean accuracy of 99% and a mean sensitivity of 99.11%.
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Affiliation(s)
- Maryam Gholami
- Department of Engineering, Islamic Azad University of Kazerun, Kazerun, Fars Iran
| | - Mahsa Maleki
- Faculty of Mechanical Engineering, University of Guilan, P.O. Box 41938-33697, Rasht, Guilan Iran.,Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, Guilan 41996-13776 Iran
| | - Saeed Amirkhani
- Faculty of Mechanical Engineering, University of Guilan, P.O. Box 41938-33697, Rasht, Guilan Iran.,Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, Guilan 41996-13776 Iran
| | - Ali Chaibakhsh
- Faculty of Mechanical Engineering, University of Guilan, P.O. Box 41938-33697, Rasht, Guilan Iran.,Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, Guilan 41996-13776 Iran
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45
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A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification. Bioengineering (Basel) 2022; 9:bioengineering9040152. [PMID: 35447712 PMCID: PMC9025942 DOI: 10.3390/bioengineering9040152] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 02/07/2023] Open
Abstract
Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently occur in a human’s life. These arrhythmias may cause potentially fatal complications, which may lead to an immediate risk of life. Thus, the detection and classification of arrhythmias is a pertinent issue for cardiac diagnosis. (1) Background: To capture these sporadic events, an electrocardiogram (ECG), a register containing the heart’s electrical function, is considered the gold standard. However, since ECG carries a vast amount of information, it becomes very complex and challenging to extract the relevant information from visual analysis. As a result, designing an efficient (automated) system to analyse the enormous quantity of data possessed by ECG is critical. (2) Method: This paper proposes a hybrid deep learning-based approach to automate the detection and classification process. This paper makes two-fold contributions. First, 1D ECG signals are translated into 2D Scalogram images to automate the noise filtering and feature extraction. Then, based on experimental evidence, by combining two learning models, namely 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) network, a hybrid model called 2D-CNN-LSTM is proposed. (3) Result: To evaluate the efficacy of the proposed 2D-CNN-LSTM approach, we conducted a rigorous experimental study using the widely adopted MIT–BIH arrhythmia database. The obtained results show that the proposed approach provides ≈98.7%, 99%, and 99% accuracy for Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR), respectively. Moreover, it provides an average sensitivity of the proposed model of 98.33% and a specificity value of 98.35%, for all three arrhythmias. (4) Conclusions: For the classification of arrhythmias, a robust approach has been introduced where 2D scalogram images of ECG signals are trained over the CNN-LSTM model. The results obtained are better as compared to the other existing techniques and will greatly reduce the amount of intervention required by doctors. For future work, the proposed method can be applied over some live ECG signals and Bi-LSTM can be applied instead of LSTM.
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46
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Conception and realization of an IoT-enabled deep CNN decision support system for automated arrhythmia classification. JOURNAL OF INTELLIGENT SYSTEMS 2022. [DOI: 10.1515/jisys-2022-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.
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47
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Li H, Boulanger P. Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features. SENSORS 2022; 22:s22072467. [PMID: 35408081 PMCID: PMC9002895 DOI: 10.3390/s22072467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/18/2022] [Accepted: 03/18/2022] [Indexed: 02/05/2023]
Abstract
Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Therefore, early detection and treatment are critical to help improve this situation. Many manufacturers have developed products to monitor patients’ heart conditions as they perform their daily activities. However, very few can diagnose complex heart anomalies beyond detecting rhythm fluctuation. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram of the ECG signal with handcrafted features to detect heart anomalies beyond commercial product capabilities. Using the proposed Convolutional Neural Network, the algorithm can detect 16 different rhythm anomalies with an accuracy of 99.79% with 0.15% false-alarm rate and 99.74% sensitivity. Additionally, the same algorithm can also detect 13 heartbeat anomalies with 99.18% accuracy with 0.45% false-alarm rate and 98.80% sensitivity.
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48
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Automatic classification of electrocardiogram signals based on transfer learning and continuous wavelet transform. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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49
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Mukhopadhyay SK, Krishnan S. Visual saliency detection approach for long-term ECG analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106518. [PMID: 34808531 DOI: 10.1016/j.cmpb.2021.106518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 11/03/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Detection and analysis of QRS-complex as well as the processing of electrocardiogram (ECG) signal using computers are being practiced for over the last fifty-eight years, approximately, and yet the thirst of designing superior ECG processing and recognition algorithms still captures researchers' attention around the globe. A saliency detection-based technique for the processing of one-dimensional biomedical signals such as ECG is proposed here for the first time, to the best or our knowledge. METHODS AND RESULTS In this proposed research work, first, a trigonometric threshold-based technique is used to identify the QRS-complexes from the ECG signal. Motion-artifact (MA) and sudden-change-in-baseline (SCB) types of noises are considered to be the toughest among others to filter out from the ECG signals as the bandwidths of these two types of noises overlap with that of the ECG. Only one feature is extracted from each of the QRS-complex-intervals, and the normalised values of this feature are arranged in the form of a gray-scale image. Then, a saliency detection-based technique is applied iteratively on the gray-scale image to detect those regions of the ECG signals, which are highly corrupted with MA and (or) SCB noises. Next, three unique geometric-features are extracted from the rest of the QRS-complexes, which are not corrupted with MA or SCB noises, and the normalised values of these three features are arranged in the form of an Red-Green-Blue (RGB) image. Again, the saliency detection-based technique is applied to identify the abnormal QRS-complexes from the RGB image. CONCLUSIONS The technique is tested on long-term ECG signals; totaling a duration of 17.54 days, and its performance is evaluated through both quantitative and qualitative measures. The applicability, scope of implement in real-time scenarios, advantage of the proposed technique over the existing ones are discussed with a group of clinicians and cardiologists, and very affirmative and encouraging responses are received from them.
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Affiliation(s)
- Sourav Kumar Mukhopadhyay
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada.
| | - Sridhar Krishnan
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada.
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Feng P, Fu J, Ge Z, Wang H, Zhou Y, Zhou B, Wang Z. Unsupervised semantic-aware adaptive feature fusion network for arrhythmia detection. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.09.046] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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