Unsupervised ECG Analysis: A Review

Abstract

Electrocardiography is the gold standard technique for detecting abnormal heart conditions. Automatic analysis of electrocardiogram (ECG) can help physicians in the interoperation of the large amount of data produced daily by cardiac monitors. As the successful application of supervised machine learning algorithms relies on unprecedented amounts of labeled training data, there is a growing need for unsupervised algorithms for ECG analysis. Unsupervised learning aims to partition ECG into distinct abnormality classes without cardiologist-supplied labelsa process referred to as ECG clustering. In addition to abnormality detection, ECG clustering can discover inter and intra-individual patterns that carry valuable information about the whole body and mind, such as emotions and mental disorders. ECG clustering can also resolve specific challenges facing supervised learning systems, such as the imbalanced data problem, and can enhance biometric systems. While several reviews exist on supervised ECG analysis, a comprehensive review of unsupervised ECG analysis techniques is still lacking. This study reviews recent ECG clustering techniques with the focus on machine learning and deep learning algorithms. We critically review and compare these techniques, discuss their applications and limitations, and provide future research directions. This review provides further insights into ECG clustering and presents the necessary information required to adopt the appropriate algorithm for a specific application.

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