Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability

Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability

A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R int...

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Bibliographic Details
Main Authors: Toshitaka Yamakawa, Miho Miyajima, Koichi Fujiwara, Manabu Kano, Yoko Suzuki, Yutaka Watanabe, Satsuki Watanabe, Tohru Hoshida, Motoki Inaji, Taketoshi Maehara
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Sensors
Subjects:
epilepsy
electrocardiography
heart rate variability
multivariate statistical process control
wearable system
machine learning
Online Access:https://www.mdpi.com/1424-8220/20/14/3987

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