Summary :
The data was is .mat format which needs to be converted to images in order to implement CNN. Fourier Transform was done to convert them into images of frequency.Then resnet50, inception_resnetv2 and effcientnetb3 was implemented. The maximum accuracy achieved is85% which was on efficientnetb3.
Abstract :
Epilepsy is a neurological disorder that disturbs the brain and causes abnormal brain activity. It results in unusual behavior, sensations and in some cases loss of awareness. In this regard, if the seizures could be identified in its earlier stages then the patient can be provided appropriate care and treatment in time and preventing any severe damage to patient as a whole. In this paper, we try to detect the epilepsy using the EEG Signal Recordings and classifying them using pre-trained CNN models between preictal and interictal classes. For this we are advocating the use of American Society for Epilpsy Dataset. The focus is on detecting the epilepsy pattern from the EEG recordings in a accurate and timely manner.