A 1D-CNN-Spectrogram Based Approach for Seizure Detection from EEG Signal

This work proposes a scheme for Seizure Detection from the Electroencephalogram (EEG) signal data generated due to the electrochemical process in the human nervous system and acquired from the human brain scalp using electrode placement. In this experiment, the complete workflow divided into three parts to get a better performance of our proposed methodology. These Parts are raw EEG Signal data filtering, Spectrogram feature matrix generation and finally, One-Dimensional Convolution Network has been used for Seizure Detection. Thus, our main objective in this work is to represent a methodology with the combination of two methods Spectrogram and 1D CNN which can be one possible approach for seizure detection. Our proposed methodology achieved comparable performance in terms of Sensitivity, Specificity, and Accuracy.