Multi-classification of arrhythmias using a HCRNet on imbalanced ECG datasets

Luo, Xinyu; Yang, Liuyang; Cai, Heng; Tang, Rui; Chen, Yu; Wei, Li

doi:10.1016/j.cmpb.2021.106258

Cited by 43 publications

(27 citation statements)

References 27 publications

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“…As summarized in Table.9, most of the works that report an overall F1 score higher than ours [21,44,78,79] performed classification for a limited 2 to 12 heart pathologies, highest F1 being 92.63% achieved by [38] for 11 classes. The current study achieves best F1 score considering 15 class heartbeat recognition.…”

Section: Discussionmentioning

confidence: 61%

“…It lacks in interpretability of the rare cases. Luo et al [44] proposed a hybrid convolutional recurrent neural net that processes time-series ECG signal and aimed to solve large imbalance in samples by a synthetic minority oversampling technique. It calculates nearest neighbors by Euclidean distance between data.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the cardiac beat classification algorithms proposed in literature (see Section-IV) use computationally intense feature extraction step after the beat segmentation (the beat segmentation criteria may be different than the one used by us i.e., some authors use 5, 6 or 10-second signal classifying rhythm rather than exact beat labels as provided by MIT-BIH data) such as frequency transforms [22,29,50,70,74,77,78,79], higher-order statistics [70,78,79,80], CNN [36,38,39,40,43,44,51], and others. Feature extraction has to be implemented on every section of the incoming timeseries ECG signal being continuously acquired by wearable device (Holter in this case).…”

Section: Introductionmentioning

confidence: 99%

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An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

et al. 2022

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Potentially lethal heart abnormalities can be detected/spotted with recent evolution in continuous, long-term cardiac health monitoring using wearable sensors. However, the huge data accumulated presents a challenge in terms of storage, knowledge extraction and computing time. Moreover, manual examination of long-term ECG recordings presents various problems like huge time and work demand, inter-observer variations and difficulty classifying complex non-linear single-lead ECG signal. To address these problems, we propose an automatic heartbeat classification system that uses the optimized minimum number of features using ECG time-series amplitude directly as input, without feature extraction and provides a primary classification and diagnosis for 1 normal and 14 types of arrhythmic heartbeats. Multi-objective particle swarm optimization (MOPSO) is used to achieve the best feature fitness. A novel fitness function is designed to be the sum of macro F1 loss and normalized dimension, with the optimization objective calculated as the minimum of the fitness function. Multi-layer perceptron (MLP), k-nearest neighbor, support vector machine, random forest and extra decision tree classifiers are trained using the selected features. For the targeted 15-class classification problem, MOPSO-optimized features with MLP consistently performed best with significantly reduced number of features. The proposed method proves to be an efficient and effective arrhythmia identification system for continuous, long-term cardiac health monitoring using single-lead ECG signal.

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Section: Discussionmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

et al. 2022

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“…In order to make a fair benchmarking, we compare our proposed model with several previous studies. These studies focus on the ECG signal classification using DL architecture, especially the use of 1D-CNN architecture ( Yıldırım, Pławiak & Rajendra Acharya, 2018 ; Rajkumar, Ganesan & Lavanya, 2019 ; Nannavecchia et al, 2021 ), LSTM architecture ( Yildirim et al, 2019 ; Gao et al, 2019 ), and combination architecture of 1D-CNN as a feature extraction and LSTM as a classifier ( Lui & Chow, 2018 ; Oh et al, 2018 ; Yildirim et al, 2020 ; Chen et al, 2020 ; Luo et al, 2021 ). However, all the classification methodologies are developed by treating beat and rhythm separately.…”

Section: Resultsmentioning

confidence: 99%

“…Combination of 1D Convolutional layers with other DL architecture can actually produce quite impressive results ( Lui & Chow, 2018 ; Oh et al, 2018 ; Chen et al, 2020 ; Yildirim et al, 2020 ; Luo et al, 2021 ) rivalling a model which uses individual 1D-CNN and LSTM architecture ( Yıldırım, Pławiak & Rajendra Acharya, 2018 ; Gao et al, 2019 ; Rajkumar, Ganesan & Lavanya, 2019 ; Nannavecchia et al, 2021 ). Even in other study, in order to obtain satisfactory results in ECG signal classification, three different DL architecture, 1D-CNN, LSTM and GRU are combined ( Luo et al, 2021 ). Unfortunately, they used SMOTE algorithm to eliminate the imbalanced problems.…”

Section: Resultsmentioning

confidence: 99%

Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification

Darmawahyuni

Nurmaini

Rachmatullah

et al. 2022

PeerJ Computer Science

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Background Electrocardiogram (ECG) signal classification plays a critical role in the automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be recognized by a human interpreter, they can be detected with precision using artificial intelligence approaches, making the ECG a powerful non-invasive biomarker. However, performing rapid and accurate ECG signal classification is difficult due to the low amplitude, complexity, and non-linearity. The widely-available deep learning (DL) method we propose has presented an opportunity to substantially improve the accuracy of automated ECG classification analysis using rhythm or beat features. Unfortunately, a comprehensive and general evaluation of the specific DL architecture for ECG analysis across a wide variety of rhythm and beat features has not been previously reported. Some previous studies have been concerned with detecting ECG class abnormalities only through rhythm or beat features separately. Methods This study proposes a single architecture based on the DL method with one-dimensional convolutional neural network (1D-CNN) architecture, to automatically classify 24 patterns of ECG signals through both rhythm and beat. To validate the proposed model, five databases which consisted of nine-class of ECG-base rhythm and 15-class of ECG-based beat were used in this study. The proposed DL network was applied and studied with varying datasets with different frequency samplings in intra and inter-patient scheme. Results Using a 10-fold cross-validation scheme, the performance results had an accuracy of 99.98%, a sensitivity of 99.90%, a specificity of 99.89%, a precision of 99.90%, and an F1-score of 99.99% for ECG rhythm classification. Additionally, for ECG beat classification, the model obtained an accuracy of 99.87%, a sensitivity of 96.97%, a specificity of 99.89%, a precision of 92.23%, and an F1-score of 94.39%. In conclusion, this study provides clinicians with an advanced methodology for detecting and discriminating heart abnormalities between different ECG rhythm and beat assessments by using one outstanding proposed DL architecture.

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