Journal article
Personalized ECG monitoring and adaptive machine learning
Journal of electrocardiology, Vol.82, pp.131-135
01/2024
DOI: 10.1016/j.jelectrocard.2023.12.006
PMCID: PMC10843583
PMID: 38128158
Abstract
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.
Details
- Title: Subtitle
- Personalized ECG monitoring and adaptive machine learning
- Creators
- Vladimir ShustermanBarry London - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of electrocardiology, Vol.82, pp.131-135
- DOI
- 10.1016/j.jelectrocard.2023.12.006
- PMID
- 38128158
- PMCID
- PMC10843583
- NLM abbreviation
- J Electrocardiol
- eISSN
- 1532-8430
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health; DOI: 10.13039/100000050, name: NHLBI, award: HL0771160, HL114277
- Language
- English
- Electronic publication date
- 12/13/2023
- Date published
- 01/2024
- Academic Unit
- Molecular Physiology and Biophysics; Cardiovascular Medicine; Internal Medicine
- Record Identifier
- 9984533284902771
Metrics
8 Record Views