Accepted for/Published in: JMIR Neurotechnology
Date Submitted: Jul 8, 2023
Date Accepted: Nov 21, 2023
Application of Low-cost Mobile Health for Remote Monitoring of Epilepsy Patients
ABSTRACT
Background:
The objective of this study is to investigate the feasibility of smartphones for processing larger electroencephalography (EEG) recordings for the application towards remote monitoring of epilepsy patients.
Objective:
We have developed a mobile application to automatically analyze and perform the classification of epileptic seizures.
Methods:
For this purpose, we have used the cross-database model developed in our previous study using successive decomposition index and matrix determinant as features, adaptive median feature baseline correction to overcome inter-database feature variation and post-processing based support vector machine for classification using five different EEG databases. The sezect (seizure detect) Android application was built using Chaquopy soft- ware development kit which uses Python language in Android Studio.
Results:
Different duration of EEG signals was tested on different versions of smartphones using sezect app to check its feasibility.
Conclusions:
The computational time required to process the real-time EEG data on smartphone and classification results suggests that mobile-health could be a great asset to monitor epilepsy patients. More details on sezect Android app can be found at: http://doi.org/10.5281/zenodo.3592415.
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