A deep learning approach for detecting epileptic spike in magnetoencephalography signals

Epilepsy is one of the most serious neurological disorders that affects people of all ages. In Canada, an average of 15,500 people discover epilepsy symptoms each year [1]. Numerous scholars have conducted extensive research in automated detection of epilepsy spike for presurgical assessment. However, the study of Magnetoencephalography (MEG) spike detection is limited to under 30 patients' data. In this thesis, we explore a deep learning approach for detecting spike in interictal MEG recordings of up to 300 epileptic patients in an automated fashion. We evaluate the convolutional neural network architecture and long short-term memory method on both 2D images and 3D spatiotemporal MEG recordings. For 2D images, we tested a simple 3 layer Convolutional Neural Network (CNN/ConvNet) model and a transfer learning model, and achieved an accuracy of 83.12% and 82.73%, sensitivity of 91.66% and 78.52%, and specificity of 74.58% and 86.94%. For 3D spatiotemporal data, we tested the 3 dimensional CNN model and Long short-term memory (LSTM) model to get 86.04% and 83.09% in accuracy, 92.37% and 87.18% in sensitivity, and 79.69% and 78.99% in specificity. The methods show an increasing performance with larger datasets, which provide us confidence on the validity of the proposed automation technique.