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.

这篇非技术综述介绍了个性化心电图监测 (pECG) 的关键概念，旨在优化临床事件及其警告信号的检测以及警报阈值的选择。我们回顾了几种 pECG 方法，包括异常检测和自适应机器学习 (ML)，其中学习是在收集新数据时顺序执行的。

我们描述了一个分布式网络多尺度 pECG 系统，以展示如何优化与自适应 ML 相关的计算负载和时间。在此架构中，ECG 波形的有限分析在本地（例如，在智能手机上）执行，以确定少量临床上重要的 ECG 元素，并且自适应 ML 引擎位于远程服务器（互联网云）上以确定个人的“指纹”基本模式并检测这些模式中的异常情况。