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Accepted for/Published in: JMIR Neurotechnology

Date Submitted: Jul 8, 2023
Date Accepted: Nov 21, 2023

The final, peer-reviewed published version of this preprint can be found here:

Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation

Sriraam N, Raghu S, D Gommer E, M W Hilkman D, Temel Y, Vasudeva Rao S, Hegde A, Kubben P

Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation

JMIR Neurotech 2023;2:e50660

DOI: 10.2196/50660

PMCID: 12671322

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Application of low-cost mobile health for remote monitoring of epilepsy patients

  • Natarajan Sriraam; 
  • S Raghu; 
  • Erik D Gommer; 
  • Danny M W Hilkman; 
  • Yasin Temel; 
  • Shyam Vasudeva Rao; 
  • AS Hegde; 
  • Pieter Kubben

ABSTRACT

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. We have developed a mobile application to automatically analyze and perform the classification of epileptic seizures. 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. Different duration of EEG signals was tested on different versions of smartphones using sezect app to check its feasibility. 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.


 Citation

Please cite as:

Sriraam N, Raghu S, D Gommer E, M W Hilkman D, Temel Y, Vasudeva Rao S, Hegde A, Kubben P

Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation

JMIR Neurotech 2023;2:e50660

DOI: 10.2196/50660

PMCID: 12671322

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