Abstract
The development of next generation wireless communication technology and Artificial Intelligence (AI) techniques to handle massive data has led to the usage of smart systems to improve the quality of human life. One such significant breakthrough is the use of smart healthcare systems. In this project, a Deep Learning (DL) based pathology detection system is suggested. Convolutional Neural Network (CNN) is used to classify and determine whether the signal belongs to a pathological individual or a normal one from the EEG (Electroencephalogram) signal. For this project, publicly available EEG signal data were used. The data signal is preprocessed to remove noise data using a Finite Impulse Response (FIR) filter. The dataset is divided in the ratio of 7:3 into train and test data. Using test data for validation, the model is found to predict at 98.0% accuracy, 96.67% specificity, and 100.0% sensitivity.
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Kavitha, M., Cyndhiya, J.J., Srinivasan, R., Kavitha, R. (2023). Brain Pathology Detection Using Convolutional Neural Network from EEG Signal. In: Murthy, B.K., Reddy, B.V.R., Hasteer, N., Van Belle, JP. (eds) Decision Intelligence. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-99-5997-6_23
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DOI: https://doi.org/10.1007/978-981-99-5997-6_23
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