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.

中文翻译：

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未来的机器学习和可穿戴设备

机器学习 (ML) 越来越被认为是包括癫痫在内的医疗保健应用中的有用工具。ML 在癫痫中最重要的应用之一是使用可穿戴设备 (WD) 检测和预测癫痫发作。但是，并非所有在 WD 中实现的当前可用算法都使用 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。已发表的证据表明，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 WD 记录的大量患者的数据应用 ML 可以从根本上改变我们诊断和管理癫痫患者的方式。