Channel selection and seizure detection using a statistical approach

This paper proposes a novel patient-specific approach to channel selection and seizure detection based on estimating the histograms of multi-channel scalp electroencephalography (sEEG) signals. It consists of two main phases: training and testing. In the training phase, the signal is segmented into non-overlapping 10-second segments, with five histograms estimated for each segment. These histograms have multiple bins that are studied individually as random variables. Based on the histograms of these random variables for different signal activities and on predefined detection and false alarm probability thresholds, bin(s) are selected form certain channel distributions for seizure detection. In selecting the training hours, a leave-one-out cross-validation strategy is adopted. In the testing phase, those channel(s)-histogram(s)-bin(s) are used to classify each segment as ictal or non-ictal. This sequence is filtered with a moving average filter and compared to a patient-specific detection threshold. This method was evaluated using 309.9 h of sEEG including 26 seizures of five patients. It achieved an average sensitivity of 97.14% and an average specificity of 98.58%.