Integrated CWT-CNN for Epilepsy Detection Using Multiclass EEG Dataset

Electroencephalography is a common clinical procedure to record brain signals generated by human activity. EEGs are useful in Brain controlled interfaces and other intelligent Neuroscience applications, but manual analysis of these brainwaves is complicated and time-consuming even for the experts of neuroscience. Various EEG analysis and classification techniques have been proposed to address this problem however, the conventional classification methods require identification and learning of specific EEG characteristics beforehand. Deep learning models can learn features from data without having in depth knowledge of data and prior feature identification. One of the great implementations of deep learning is Convolutional Neural Network (CNN) which has outperformed traditional neural networks in pattern recognition and image classification. Continuous Wavelet Transform (CWT) is an efficient signal analysis technique that presents the magnitude of EEG signals as time-related Frequency components. Existing deep learning architectures suffer from poor performance when classifying EEG signals in the Time-frequency domain. To improve classification accuracy, we propose an integrated CWT and CNN technique which classifies five types of EEG signals using. We compared the results of proposed integrated CWT and CNN method with existing deep learning models e.g., GoogleNet, VGG16, AlexNet. Furthermore, the accuracy and loss of the proposed integrated CWT and CNN method have been cross validated using Kfold cross validation. The average accuracy and loss of Kfold cross-validation for proposed integrated CWT and CNN method are, 76.12% and 56.02% respectively. This model produces results on a publicly available dataset: Epilepsy dataset by UCI (Machine Learning Repository).