Review of ECG detection and classification based on deep learning: Coherent taxonomy, motivation, open challenges and recommendations

Chen, Shan Wei; Wang, Shir Li; Qi, Xiu Zhi; Samuri, Suzani Mohamad; Yang, Can

doi:10.1016/j.bspc.2022.103493

Cited by 32 publications

(16 citation statements)

References 88 publications

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“…Not many works are focused on studying how a CVD can affect the identification of users through their EKG as it is done in this thesis. However, studies of classification techniques of different CVD such as Myocardial Infarction, Arrhythmia, Ventricular Ectopic Beats (VEB) and Supraventricular Ectopic Beats (SVEB) among others are very common (see [170,171,172,173,174]).…”

Section: Discussionmentioning

confidence: 99%

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

Fuster-Barceló

Peris-López²,

Cámara³

2022

Neurocomputing

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Section: Discussionmentioning

confidence: 99%

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

Fuster-Barceló

Peris-López²,

Cámara³

2022

Neurocomputing

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“…ResNet uses crosslayer connections and stacked residual blocks [3] to increase network depth while reducing the gradient disappearance problem. ResNet has been shown to have excellent performance in ECG classification [10] .…”

Section: Related Workmentioning

confidence: 99%

“…Figure 3. The complete convolution process with FFC-R When we use FFC-R to classify ECG signals, the first step is to convert the input one-dimensional signal into a singlechannel [10] image of 500 × 12. This can be seen as a two-dimensional image formed by arranging the signal sequence in time steps.…”

Section: Our Proposed Ffc-rmentioning

confidence: 99%

“…By analyzing various information in electrocardiograms and premature ventricular contractions on time, the risk of cardiac disease and sudden cardiac death can be greatly reduced. In scientific research on heart disease, the application of deep learning-based automatic analysis and identification techniques for electrocardiogram signals can improve diagnostic accuracy [1,10] . In recent years, deep learning techniques have been widely used in ECG classification [2] because they can automatically extract and learn features, thereby improving the accuracy and efficiency of classification.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic classification and detection of 12-lead electrocardiogram signal classification with Fourier convolutions

Liu

Yao

et al. 2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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In this paper, we propose an Electrocardiogram (ECG) classification model based on FFC (Fast Fourier Convolution) and ResNet. The model utilizes FFC and ResNet for feature extraction and classification. We further improve the network performance and convergence speed through batch normalization and residual concatenation. The experimental results demonstrate that the model exhibits excellent classification performance under different data distributions in the PTB-XL database and trains faster than traditional ResNet models. Additionally, we introduce a new module, FFC-R, and validate its excellent performance in ECG classification tasks. This innovation is expected to provide powerful support for the diagnosis and treatment of heart diseases.

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“…In general, we believe that with this study, we (1) provided a complete and systematic account of the current state-of-the-art DL methods applied to ECG data; (2) identified several ECG data sources used in clinical diagnosis, even some not so widely cited databases; and ( 3) identified important open research problems and provided suggestions for future research directions in the field of DL and ECG data. Several important relevant review studies have already presented novel DL methods that are used on ECG data [355][356][357]. Nonetheless, none of them combine all the aforementioned characteristics, which makes this study innovative.…”

Section: Principal Findingsmentioning

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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Review of ECG detection and classification based on deep learning: Coherent taxonomy, motivation, open challenges and recommendations

Cited by 32 publications

References 88 publications

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

Automatic classification and detection of 12-lead electrocardiogram signal classification with Fourier convolutions

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

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