Machine learning and wearable devices of the future
Machine learning and wearable devices of the future
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DOI：
10.1111/epi.16555
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发表时间：
2020-07-26
期刊：
影响因子：
5.6
通讯作者：
Cook, Mark
中科院分区：
文献类型：
--
作者：
Beniczky, Sandor;Karoly, Philippa;Cook, Mark
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（WDS）进行癫痫发作和预测。但是，并非所有当前在WDS中实施的算法都使用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.有证据表明使用植入脑电图（EEG）电极和可穿戴的非EEG设备可靠检测癫痫发作。使用来自大量患者的WD记录的数据应用ML可能会以我们诊断和管理癫痫患者的方式改变。
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.