| 초록 |
Although many studies have analyzed the relationship between electrodermal activity (EDA) and sleep stages, a practical method for detecting sleep stage using EDA has not been suggested. The aim of this study was to develop an algorithm for real-time automatic detection of deep sleep using the EDA signal. Simultaneously with overnight polysomnography (PSG), continuous measurement of skin conductance on the fingers was performed for ten subjects. The morphometric characteristics in the fluctuations of EDA signal were employed to establish the quantitative criteria for determining deep sleep. The 30-sec epoch-by-epoch comparison between the deep sleep detected by our method and that reported from PSG exhibited an average sensitivity of 74.6%, an average specificity of 98.0%, and an average accuracy of 96.1%. This study may address the growing need for a reliable and simple measure for identifying sleep stage without a PSG. |
