Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping

Epilepsy is a brain disorder that may strike at different stages of life. Patients' lives are extremely disturbed by the occurrence of sudden unpredictable epileptic seizures. A possible approach to diagnose epileptic patients is to analyze magnetoencephalography (MEG) signals to extract useful information about subject's brain activities. MEG signals are less distorted than electroencephalogram signals by the intervening tissues between the neural source and the sensor (e.g., skull, scalp, and so on), which results in a better spatial accuracy of the MEG. This paper aims to develop a method to detect epileptic spikes from multi-channel MEG signals in a patient-independent setting. Amplitude thresholding is first employed to localize abnormalities and identify the channels where they exist. Then, dynamic time warping is applied to the identified abnormalities to detect the actual epileptic spikes. The sensitivity and specificity of proposed detection algorithm are 92.45% and 95.81%, respectively. These results indicate that the proposed algorithm can help neurologists to analyze MEG data in an automated manner instead of spending considerable time to detect MEG spikes by visual inspection.