Sleep staging from the visibility graph algorithm of series

Monitoring the sleep quality accurately can play an effective supporting role in helping people improve the quality of sleep. In the present study, a novel feature extraction algorithm is proposed based on the natural visibility graph and horizontal visibility graph methods. The slope of visibility degree distribution, the mean of visibility distance, the mean of averaged visibility distance and the mean of improved weighted visibility graph were extracted, and trained by the least square-support vector machines (LS-SVM) classifier. The mathematical model between electroencephalogram (EEG) and sleep state was established and verified by different samples. The results demonstrated that the classification accuracy of different states improved about 5.72% compared to the existing weighted visibility graph, the classification accuracy of shallow sleep states improved about 9.65%.