@inproceedings{140d356e699f4e8c810db2af515decfa,
title = "Initial Design of Wearable EEG Device for Epilepsy Patient Using Machine Learning and Mobile Application",
abstract = "Epilepsy is a neural tissue disease that affects many patients worldwide, including in developing countries. A person who has epilepsy can easily experience attacks suddenly and at any time, making it difficult to carry out daily activities. Early medical diagnosis and treatment are essential to help people with epilepsy. The system developed in this study assists significantly in collecting data on the patient's nervous condition to obtain accurate data as the basis for medical diagnosis by doctors. It was difficult before to do it in the current system, where detecting epileptic seizures requires manual observation of patient videos. A wearable EEG device combined with the mobile application reduces the patient's burden because they don't have to stay in one place during the EEG data collection. Furthermore, the machine learning method developed in this study helped doctors identify the predictive time and pattern of an epileptic attack more accurately and thoroughly.",
keywords = "Brain waves, EEG, Machine Learning, Mobile, epilepsy",
author = "F. Fahmi and Wervyan Shalannanda and Muhammad Yazid and Erwin Sutanto",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023 ; Conference date: 10-11-2023 Through 11-11-2023",
year = "2023",
doi = "10.1109/ICoSNIKOM60230.2023.10364501",
language = "English",
series = "2023 IEEE International Conference of Computer Science and Information Technology: The Role of Artificial Intelligence Technology in Human and Computer Interactions in the Industrial Era 5.0, ICOSNIKOM 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE International Conference of Computer Science and Information Technology",
address = "United States",
}