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Machine learning and wearable devices of the future
Sándor Beniczky1,2,3, Philippa Karoly4, Ewan Nurse4
1Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.
Epilepsia
|July 27, 2020
Summary
Machine learning (ML) in wearable devices (WDs) shows promise for epilepsy seizure detection and prediction. Future applications of ML with WD data could significantly improve epilepsy diagnosis and management.
Area of Science:
- Neurology
- Biomedical Engineering
- Computer Science
Background:
- Machine learning (ML) is a growing tool in healthcare, particularly for epilepsy management.
- Wearable devices (WDs) are being developed for monitoring epilepsy patients.
- Current WDs for epilepsy do not universally employ ML algorithms.
Purpose of the Study:
- To review the current state of wearable devices (WDs) and machine learning (ML) in epilepsy.
- To outline future directions for WDs and ML in epilepsy care.
Main Methods:
- Review of published evidence on WDs and ML for epilepsy.
- Analysis of seizure detection and prediction capabilities using WDs.
- Examination of data from implanted electroencephalography (EEG) and non-EEG WDs.
Main Results:
- Evidence supports reliable epileptic seizure detection using both implanted EEG and non-EEG WDs.
- ML algorithms applied to WD data show potential for epilepsy management.
- Large-scale data collection via WDs could revolutionize epilepsy diagnosis.
Conclusions:
- WDs combined with ML offer a promising approach for epilepsy management.
- Further development and application of ML in WDs are crucial for advancing epilepsy care.
- The integration of ML and WDs has the potential to transform epilepsy diagnosis and patient management.
