Machine learning and wearable devices of the future

被引:94
作者
Beniczky, Sandor [1 ,2 ,3 ]
Karoly, Philippa [4 ]
Nurse, Ewan [4 ]
Ryvlin, Philippe [5 ]
Cook, Mark [4 ]
机构
[1] Danish Epilepsy Ctr, Dept Clin Neurophysiol, Dianalund, Denmark
[2] Aarhus Univ Hosp, Dept Clin Neurophysiol, Aarhus, Denmark
[3] Aarhus Univ, Dept Clin Med, Aarhus, Denmark
[4] Univ Melbourne, Graeme Clark Inst, Melbourne, Vic, Australia
[5] CHU Vaudois, Dept Clin Neurosci, Lausanne, Switzerland
关键词
epilepsy; machine learning; seizure detection; seizure prediction; wearable devices; DRUG-RESISTANT EPILEPSY; SEIZURE DETECTION; SURFACE ELECTROMYOGRAPHY; QUANTITATIVE-ANALYSIS; INTERICTAL BURSTS; SPIKE DETECTION; PREDICTION; EEG; MULTICENTER; PATIENT;
D O I
10.1111/epi.16555
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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.
引用
收藏
页码:S116 / S124
页数:9
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