Development of a human-computer collaborative sleep scoring system for polysomnography recordings

Sheng-Fu Liang, Yu-Hsuan Shih, Peng-Yu Chen and Chih-En Kuo

PLOS ONE, 2019, vol. 14, issue 7, 1-15

Abstract: The overnight polysomnographic (PSG) recordings of patients were scored by an expert to diagnose sleep disorders. Visual sleep scoring is a time-consuming and subjective process. Automatic sleep staging methods can help; however, the mechanism and reliability of these methods are not fully understood. Therefore, experts often need to rescore the recordings to obtain reliable results. Here, we propose a human-computer collaborative sleep scoring system. It is a rule-based automatic sleep scoring method that follows the American Academy of Sleep Medicine (AASM) guidelines to perform an initial scoring. Then, the reliability level of each epoch is analyzed based on physiological patterns during sleep and the characteristics of various stage changes.Finally, experts would only need to rescore epochs with a low-reliability level. The experimental results show that the average agreement rate between our system and fully manual scorings can reach 90.42% with a kappa coefficient of 0.85. Over 50% of the manual scoring time can be reduced. Due to the demonstrated robustness and applicability, the proposed approach can be integrated with various PSG systems or automatic sleep scoring methods for sleep monitoring in clinical or homecare applications in the future.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0218948

DOI: 10.1371/journal.pone.0218948

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