Enhancing Seizure Detection:A Deep Learning Approach With Non-Linear Bilateral Filtering

IPEM Journal Computer Applications > Enhancing Seizure Detection:A Deep Learning Approach With Non-Linear Bilateral Filtering

Enhancing Seizure Detection:A Deep Learning Approach With Non-Linear Bilateral Filtering

Ashish Sharma , Vinai K Singh

Vol 8 , Issue 1 , December 2023 | Pages: 43-47

DOI: 10.61691/IPEM_CA.8.2023.43-47

Published Online: December, 2023

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Author Details

1. Ashish Sharma, Motherhood University, Roorkee, Haridwar, Uttarakhand, India
2. Vinai K Singh, Motherhood University, Roorkee, Haridwar, Uttarakhand, India

Seizure detection is an essential component of patient care in the management of epilepsy. This paper introduces a novel method for improving seizure detection using a deep learning model and integrating non-linear bilateral filtering. Deep learning has demonstrated significant potential in examining intricate medical data, and in this study, it is utilised to examine electroencephalogram (EEG) signals to identify seizures. Integrating non-linear bilateral filtering enhances the initial processing of EEG data, resulting in improved accuracy in extracting features and classifying the data. The results indicate the possibility of substantial progress in seizure detection, providing more precise and dependable early detection and intervention techniques in treating epilepsy.

Keywords

Deep learning CNN model, electroencephalogram (EEG), epilepsy, seizure detection, binary classification.
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Sharma, et.al (2003);Enhancing Seizure Detection: A Deep Learning Approach with Non-Linear Bilateral Filtering,IPEM JOURNAL OF COMPUTER APPLICATION & RESEARCH, 8(1), 43-47

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