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Bhargavi Marathe,
Jaya Jeswani,
Sarthak Patil,
Rushikesh Redij,
- Student, Department of Information Technology Xavier Institute of Engineering Mumbai, Maharashtra, India
- Professor,, Department of Information Technology Xavier Institute of Engineering Mumbai, Maharashtra, India
- Student, Department of Information Technology Xavier Institute of Engineering Mumbai, Maharashtra, India
- Student, Department of Information Technology Xavier Institute of Engineering Mumbai, Maharashtra, India
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For a proper diagnosis and prompt treatment, epilepsy, a neurological condition marked by recurring seizures, needs to be continuously monitored. Manual interpretation is frequently used in traditional approaches for identifying epileptic seizures from electroencephalogram (EEG) signals, which can be laborious and error prone. In this research, a novel method for automatically detecting epilepsy from EEG data using deep learning algorithms is presented. According to CDC research, over 3 million Americans suffer with epilepsy. This study investigated the use of deep learning techniques for both EEG data analysis and seizure diagnosis. Here, a method known as 1D CNN-LSTM is used, which combines long short-term memory with a dimensional convolution neural network. Whereas the lengthy short-term memory component concentrates on extracting temporal details, CNN mainly pulls spatial features from the standardized EEG sequence analysis. The CBHMIT dataset is used in the study, with 80% of it being randomly divided into training and 20% into testing. Accuracy rates of up to 98% have been attained with the use of the CNN architecture, with CNN-LSTM achieving an average accuracy of 85%. A very effective and reasonably priced EEG identification tool designed for epilepsy has been developed because of advances in electroencephalogram (EEG) technology.
Keywords: Epilepsy, Convolutional neural network, Convolutional neural network-long short-term memory, signal analysis, epileptic seizure recognition.
[This article belongs to Trends in Opto-electro & Optical Communication (toeoc)]
Bhargavi Marathe, Jaya Jeswani, Sarthak Patil, Rushikesh Redij. Advancing EEG Technology for Affordable and Effective Epilepsy Detection. Trends in Opto-electro & Optical Communication. 2024; 14(03):31-40.
Bhargavi Marathe, Jaya Jeswani, Sarthak Patil, Rushikesh Redij. Advancing EEG Technology for Affordable and Effective Epilepsy Detection. Trends in Opto-electro & Optical Communication. 2024; 14(03):31-40. Available from: https://journals.stmjournals.com/toeoc/article=2024/view=0
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Trends in Opto-electro & Optical Communication
| Volume | 14 |
| Issue | 03 |
| Received | 02/08/2024 |
| Accepted | 16/10/2024 |
| Published | 18/11/2024 |