Key result
Systematic review of 230 articles details deep learning architectures and preprocessing techniques for ECG data.
Why the study?
Deep learning is widely used to develop diagnostic models from physiological signals, warranting a systematic review of these methods applied to ECG data for clinical applications.
Systematic Review (n=230)
This systematic review summarizes recent deep learning methods applied to ECG data, highlighting current strategies and identifying gaps for future research in clinical decision-making models.
BACKGROUND The electrocardiogram (ECG) is one of the most common non-invasive diagnostic tools that can provide useful information regarding the patient’s health status. Deep learning (DL) is a current area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. OBJECTIVE This paper provides a systematic review of DL methods applied to ECG data for various clinical applications. METHODS We identified 230 relevant articles published between January 2020 and December 2021 and provided a complete account of the state-of-the-art DL strategies by reporting on the number and type of hidden layers, the ECG data sources, the data preprocessing techniques, and the data splitting strategies for each one of them. RESULTS We provided a complete account of the state-of-the-art DL strategies by reporting on the number and type of hidden layers, the ECG data sources, the data preprocessing techniques, and the data splitting strategies for each one of them. We also present open research problems and point out potential gaps regarding the design and implementation of DL models. CONCLUSIONS We expect this review will provide insights into the 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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Πετμεζάς et al. (2022) conducted a systematic review in ECG data analysis (n=230). Deep learning methods was evaluated on State-of-the-art DL strategies including hidden layers, data sources, preprocessing, and splitting strategies. Deep learning methods applied to ECG data were systematically reviewed across 230 articles published between 2020 and 2021, detailing network architectures, data sources, and preprocessing techniques.
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