ECG-based expert-knowledge attention network to tachyarrhythmia recognition

Tao, Yanyun; Li, Zuoyong; Gu, Chao-Chen; Bin, Jiang; Zhang, Yuzhen

doi:10.1016/j.bspc.2022.103649

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…To appraise the performance of our proposed model more thoroughly, we have conducted a comparative analysis with existing methods. This includes traditional machine learning algorithms including LDA-MLP [ 14 ], SVM [ 15 ], XGBoost [ 16 ], and RandomForest [ 17 ], as well as deep learning methods comprising CNN [ 18 ], ResNet [ 19 ], DCNN [ 20 ], CNN-BiLSTM [ 21 ], and Deep Attention BiLSTM [ 22 ]. The outcomes of this comparison are delineated in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

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Discrete Wavelet Transform based ECG classification using gcForest: A deep ensemble method

Lin,

Hong,

Hong

et al. 2024

THC

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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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Section: Resultsmentioning

confidence: 99%

“…A DCNN with raw ECG signals for inputs [ 20 ] obtained an accuracy of 91.33%. The CNN-BiLSTM [ 21 ] and Deep Attention BiLSTM [ 22 ], both ECG classification methods based on CNN and LSTM, differ in that the latter incorporates an attention mechanism. They achieved accuracies of 96.77% and 96.72%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Discrete Wavelet Transform based ECG classification using gcForest: A deep ensemble method

Lin,

Hong,

Hong

et al. 2024

THC

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“…Hence, the intra-patient diagnosis is performed where the DL model has the possibility to train and test based on ECG information from the same patient. By mixing multiple ECG datasets, the issue of imbalance in data categories can also be alleviated [22]. Regardless of inter-or intra-patient diagnosis, it shows a clear trend over the last few years that increasing studies exploit combined ECG datasets for DL-based ECG arrhythmia analysis [18,21,[23][24][25][26].…”

Section: Databasementioning

confidence: 99%

“…The discrete wavelet transform (DWT) could project ECG signals onto the time-frequency domain based on wavelet basis functions [36]. To remove the noise, the wavelet coefficients at high-frequency bands can be simply set to zero or apply a thresholding process to set the modest wavelet coefficients to zero [19][20][21][22] based on the assumption that the useful ECG signal is similar to the selected wavelet basis function. A combination of different types of denoising methods can be applied for noise removal, e.g., [37,38] combines DWT, median filters, or S-G filters for denoising.…”

Section: Denosingmentioning

confidence: 99%

Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review

et al. 2023

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Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.

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“…Based on a recent review, we see that the attentionbased model [47] can capture the most relevant features, and dilated CNN [10] [52] [14] [14] can extract features at a faster speed [32]. This further motivated us to develop a novel method for ECG classification using attention dilated CNN with Bidirectional RNN.…”

Section: Traditional Machine Learningmentioning

confidence: 99%