Multi-channel EEG-based sleep staging using brain functional connectivity and domain adaptation

Abstract

Abstract Objective. Sleep stage recognition has essential clinical value for evaluating human physical/mental condition and diagnosing sleep-related diseases. To conduct a five-class (wake, N1, N2, N3 and rapid eye movement) sleep staging task, twenty subjects with recorded six-channel electroencephalography (EEG) signals from the ISRUC-SLEEP dataset is used. Approach. Unlike the exist methods ignoring the channel coupling relationship and non-stationarity characteristics, we developed a brain functional connectivity method to provide a new insight for multi-channel analysis. Furthermore, we investigated three frequency-domain features: two functional connectivity estimations, i.e. synchronization likelihood (SL) and wavelet-based correlation (WC) among four frequency bands, and energy ratio (ER) related to six frequency bands, respectively. Then, the Gaussian support vector machine (SVM) method was used to predict the five sleep stages. The performance of the applied features is evaluated in both subject dependence experiment by ten-fold cross validation and subject independence experiment by leave-one-subject-out cross-validation, respectively. Main results. In subject dependence experiment, the results showed that the fused feature (fusion of SL, WC and ER features) contributes significant gain the performance of SVM classifier, where the mean of classification accuracy can achieve 83.97% ± 1.04%. However, in subject-independence experiment, the individual differences EEG patterns across subjects leads to inferior accuracy. Five typical domain adaptation (DA) methods were applied to reduce the discrepancy of feature distributions by selecting the optimal subspace dimension. Results showed that four DA methods can significantly improve the mean accuracy by 1.89%–5.22% compared to the baseline accuracy 57.44% in leave-one-subject-out cross-validation. Significance. Compared with traditional time-frequency and nonlinear features, brain functional connectivity features can capture the correlation between different brain regions. For the individual EEG response differences, domain adaptation methods can transform features to improve the performance of sleep staging algorithms.