Welcome to the InfoSci Platform
Reference Hub1
Low-Cost Internet of Things Platform for Epilepsy Monitoring Using Real-Time Electroencephalogram

Low-Cost Internet of Things Platform for Epilepsy Monitoring Using Real-Time Electroencephalogram

Manoj Kumar Sharma, M. Shamim Kaiser, Kanad Ray
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 14
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781683180647|DOI: 10.4018/IJACI.300791
Cite Article Cite Article

MLA

Sharma, Manoj Kumar, et al. "Low-Cost Internet of Things Platform for Epilepsy Monitoring Using Real-Time Electroencephalogram." IJACI vol.13, no.1 2022: pp.1-14. http://doi.org/10.4018/IJACI.300791

APA

Sharma, M. K., Kaiser, M. S., & Ray, K. (2022). Low-Cost Internet of Things Platform for Epilepsy Monitoring Using Real-Time Electroencephalogram. International Journal of Ambient Computing and Intelligence (IJACI), 13(1), 1-14. http://doi.org/10.4018/IJACI.300791

Chicago

Sharma, Manoj Kumar, M. Shamim Kaiser, and Kanad Ray. "Low-Cost Internet of Things Platform for Epilepsy Monitoring Using Real-Time Electroencephalogram," International Journal of Ambient Computing and Intelligence (IJACI) 13, no.1: 1-14. http://doi.org/10.4018/IJACI.300791

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This work is focusing to develop a portable, low-cost remote diagnostic system for developing countries where the current state of health is not in the advanced stage. People with diseases like epilepsy, Alzheimer’s, an extreme turmeric state, or a disorder that makes it difficult to move have been observed. The authors propose a cost-effective remote neurology assessment health care system. To predict epilepsy form electroencephalogram (EEG) signals in real-time. The authors implemented the machine learning model that has been deployed in the raspberry pi micro-controller. The feature extraction stage was carried out in Matlab. The extracted features from the EEG signals were transferred wirelessly to the model deployed in pi raspberry to clearly predict epilepsy and normality cases. The results of the real-time prediction of the trained and deployed model were provided for the remote diagnosis system. The data visualizations can be done on Android/IOS and Matlab desktop.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.