Automatic Detection of Sleep Arousal Events from Polysomnographic Biosignals

Abstract

Manual scoring of arousals is generally conducted by sleep experts in spite of being time-consuming and subjective. Our objective of this study was to develop an algorithm for automatic detection of sleep arousals without distinguishing between the types of arousal and sleep disorder groups. The processed and analysed data multiple overnight Polysomnography (PSG) recordings, consisting of 9 human subjects (6 male, 3 female), with age range of 34-69 and different conditions (4 patients with obstructive sleep apnoeas, 4 healthy and 1 patient with periodic limb movement disorder). PSG biosignals were processed to extract necessary features. Knearest neighbours (KNN) was used as the classifier and performance of algorithm were evaluated by Leave-One-Out Cross-Validation. The average sensitivity, specificity and accuracy of algorithm was 79%, 95.5% and 93%, respectively. These results demonstrate that our algorithm can automatically detect arousals with high accuracy. Furthermore, the algorithm is capable to be upgraded for classification of various types of arousals based upon their origin and characteristics