Personalized ECG monitoring and adaptive machine learning.

This non-technical review introduces key concepts in personalized ECG monitoring (pECG), which aims to optimize the detection of clinical events and their warning signs as well as the selection of alarm thresholds. We review several pECG methods, including anomaly detection and adaptive machine learning (ML), in which learning is performed sequentially as new data are collected. We describe a distributed-network multiscale pECG system to show how the computational load and time associated with adaptive ML could be optimized. In this architecture, the limited analysis of ECG waveforms is performed locally (e.g., on a smart phone) to determine a small number of clinically important ECG elements, and an adaptive ML engine is located on a remote server (Internet cloud) to determine an individual's "fingerprint" basis patterns and to detect anomalies in those patterns. Copyright © 2023 Elsevier Inc. All rights reserved.

Citation

Vladimir Shusterman, Barry London. Personalized ECG monitoring and adaptive machine learning. Journal of electrocardiology. 2024 Jan-Feb;82:131-135

Mesh Tags

PMID: 38128158

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