Key result
Hybrid ML and DL for ECG interpretation may improve both interpretability and hidden pattern identification.
Why the study?
Manual interpretation of the increasing volume of recorded ECGs is not feasible or scalable, prompting the need to review machine learning techniques for automated ECG interpretation.
Hybrid approaches combining traditional machine learning and deep learning offer a promising path for automated ECG interpretation by balancing interpretability with advanced pattern recognition.
Cardiac arrhythmia can lead to morbidity and mortality and is a substantial economic burden. Electrocardiogram (ECG) monitoring is widely used to detect arrhythmia. The number of ECG recordings is increasing due to aging populations and availability of easy-to-use wearable devices. Manual interpretation of the high volume of recorded ECGs might not be a feasible and scalable solution. Therefore, machine learning algorithms are widely used for automatic ECG interpretation. In this paper, the challenges and differences between machine learning techniques for ECG monitoring and interpretation are reviewed, from traditional machine learning classifiers to deep learning and their combination.
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Parvaneh et al. (2018) conducted a review in Cardiac arrhythmia. Machine learning and deep learning algorithms was evaluated. Hybrid approaches combining traditional machine learning and deep learning for automated ECG interpretation may offer the benefits of both better interpretability and hidden pattern identification.
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