KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement

Lu, Peng; Gao, Yang; Xi, Hao; Zhang, Yabin; Gao, Chao; Zhang, Hongpo; Chen, Liwei; Mao, Xiaobo

doi:10.1155/2021/6684954

Cited by 8 publications

(10 citation statements)

References 28 publications

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“…However, a high percentage of the studies that managed to classify data originating from the same database implemented a CNN structure. Lu et al [ 180 ] used a 1D-CNN for arrhythmia classification, achieving an accuracy of 99.31%, whereas Yu et al [ 219 ] used a 1D-CNN to detect premature ventricular contraction, achieving a classification accuracy of 99.70%.…”

Section: Resultsmentioning

confidence: 99%

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Petmezas¹,

Stefanopoulos²,

Kilintzis³

et al. 2022

JMIR Med Inform

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Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

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Section: Resultsmentioning

confidence: 99%

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Petmezas¹,

Stefanopoulos²,

Kilintzis³

et al. 2022

JMIR Med Inform

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“…After a full text review, 102 studies in total were included in the qualitative review. 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 …”

Section: Resultsmentioning

confidence: 99%

“…The studies related to arrhythmias accounted for the largest proportion, at 62 studies 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 ( Supplementary Table 1 , only online). Most studies had AF detection as the main task.…”

Section: Resultsmentioning

confidence: 99%

“…With the advent of deep neural networks, the competition for predictive performance has virtually reached saturation, and the development of a lightweight model that can be mounted on a wearable device and operated in real time has recently become a major topic of research. 18 27 There have been studies to determine a critical point that does not compromise the diagnostic accuracy while reducing the data size through compression of the ECG signal itself, in addition to reducing the weight of the model. 32 38 43 …”

Section: Resultsmentioning

confidence: 99%

“…Therefore, it is essential to develop a lightweight and fast model, even if there is a slight loss in performance. 18 27 32 38 43 Second, the algorithm is operated in an environment that is not well-controlled, unlike a hospital environment. As mentioned earlier, a model that is developed using resting biosignals as a source rarely works well for ambulatory biosignals.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

et al. 2022

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Purpose Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases. Materials and Methods The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity. Results A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983). Conclusion This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.

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A lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection

Alamatsaz,

Tabatabaei,

Yazdchi

et al. 2024

Biomedical Signal Processing and Control

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KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement

Cited by 8 publications

References 28 publications

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

A lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection

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