Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study.

ABSTRACT:

Background

Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices.

Method

Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project hea

SUBMITTER: Xiong W 

PROVIDER: S-EPMC10300292 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature