International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 16, No. 6, 8 Dec. 2024
Cover page and Table of Contents: PDF (size: 1975KB)
Analysis of Multichannel Neurophysiological Signal for Identifying Epileptic Cases Using Hybrid Deep-Nets
PDF (1975KB), PP.55-71
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Author(s)
Shipra Swati
1
Mukesh Kumar
1,*
Index Terms
Electroencephalogram, Deep Learning, CNN, Cognitive Dysfunction
Abstract
Neurophysiological parameters revealed by resting-state electroencephalography (rsEEG) may be helpful in the diagnosis of various brain diseases like Epilepsy, Alzheimer’s, depressive disorders, and many others. Due to the abrupt onset of seizures, Epilepsy is a chronic nerve illness that interferes with an epileptic patient's regular everyday activities. However, manual investigation of EEG for finding epileptiform discharges by skilled neurologists is a laborious, time-consuming, and error-prone process. It might cause a significant delay in providing clinical care to a person who could have epilepsy. This work offers a straightforward method for analyzing EEG data for the purpose of identifying epileptic features by iteratively simulating multiple deep learning models. It also attempts to include big data analytics for handling the challenge of analyzing the mountain of unstructured EEG data, available and accessible in numerous formats. In contrast to the state-of-the-art works, the performance scores of the proposed methods show significant improvement for the considered assessment parameters. Additionally, after testifying the performance of this proposed technique for relevant datasets, its application can be extended to identify other neurodegenerative disorders as well. Therefore, this study can assist physicians and healthcare professionals in the efficient care and treatment of patients with mental health issues.
Cite This Paper
Shipra Swati, Mukesh Kumar, "Analysis of Multichannel Neurophysiological Signal for Identifying Epileptic Cases Using Hybrid Deep-Nets", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.16, No.6, pp. 55-71, 2024. DOI:10.5815/ijigsp.2024.06.05
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