Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.
Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.
Epilepsia. 2021 Mar;62 Suppl 2:S116-S124. doi: 10.1111/epi.16555. Epub 2020 Jul 26.
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.
机器学习（ML）在医疗保健应用中，包括癫痫领域，越来越被认为是一种有用的工具。ML 在癫痫中的一个最重要的应用是使用可穿戴设备（WDs）进行癫痫发作的检测和预测。然而，并非所有目前在 WDs 中实现的算法都在使用 ML。在这篇综述中，我们总结了在癫痫中使用 WDs 和 ML 的最新技术，并概述了这些领域的未来发展。有证据表明，使用植入式脑电图（EEG）电极和可穿戴的非 EEG 设备可以可靠地检测癫痫发作。使用 WDs 从大量患者记录的数据应用 ML 可能会从根本上改变我们诊断和管理癫痫患者的方式。
