Detection of Epileptic Seizures using Novel Multi-Layered Convolution Neural Network in Comparison with Fully Convolutional Neural Network to Improve the Accuracy

This research study is to detect the Epileptic seizures using Novel Multi-Layered Convolutional Neural Networks (NMLCNN) and to compare the performance with Fully Convolutional Neural Networks (FCNN). By using Novel Multi-Layered Convolution Neural Networks algorithm works with the data collected from the observations of nervous system cells. The changes in the Epileptic seizures from the resource’s constraint, when the criteria met with randomizing, this will arrange in random ways for n times. The most typical beginning of a seizure is detectable through analysis of the Electroencephalogram (EEG). To examine this, the study utilized 20 samples in each group, with a Gpower value of 80%. The study used a predefined significance value and a Confidence Interval (CI) of 95%.This experiment found that NMLCNN has 92.25% and FCNN has 87.55% of accuracy for epileptic seizure. The Novel Multi-Layered Convolutional Neural Network achieved 0.012 significance using group analysis and provided significance. This probing concludes based on accuracy and significance results, detection of Epileptic seizures using Novel Multi-layered Convolutional Neural Networks has performed better compared to Fully Convolutional Neural Network.