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Article type: Research Article
Authors: Goel, Sachina; * | Agrawal, Rajeevb | Bharti, R.K.c
Affiliations: [a] Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun, India | [b] Lloyd Institute of Engineering & Technology, Greater Noida, India | [c] Bipin Tripathi Kumaon Institute of Technology, Dwarahat, Uttarakhand, India
Correspondence: [*] Corresponding author. Sachin Goel, Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun, India. E-mail: [email protected].
Abstract: Epilepsy is the most common neurological disorder by which over 65 million people are affected across the world. Recent research has shown a very large interest to predict and diagnose epilepsy well before time. The continuous monitoring of EEG signals for seizure detection in electroencephalogram (EEG) is a very tedious and time taking process and therefore requires a qualified and trained clinical specialist. This paper presents a novel approach to detect and predict the epileptic signal in the recorded electroencephalogram (EEG). There is always a requirement for a nonlinear technique to examine the EEG signals due to the random nature of EEG signals. Therefore, we are providing an alternate method that extracts various entropy measures such Sample Entropy, Spectral Entropy, Permutation Entropy, and Shannon Entropy as statistical features from EEG signal. Based on these extracted features LSTM Fully connected Neural Network is used to classify the EEG signals as Focal and Non-focal. The proposed method gives a new insight into EEG signals by providing sensitivity as an added measure using deep learning along with accuracy and precision.
Keywords: Epilepsy, focal & non-focal classification, LSTM, entropy measures
DOI: 10.3233/JIFS-222745
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6003-6020, 2023
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