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. 2019 Jul 10;14(7):e0218948.
doi: 10.1371/journal.pone.0218948. eCollection 2019.

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

Sheng-Fu Liang  1   2 , Yu-Hsuan Shih  1 , Peng-Yu Chen  1 , Chih-En Kuo  3
Affiliations

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

Sheng-Fu Liang et al. PLoS One. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The flow chart of the human-computer collaborative sleep scoring system.
The system consists of (A) fully automatic scoring, (B) reliability analysis, and (C) human-computer collaboration.
Fig 2
Fig 2. Histogram of 12 sleep features in Wake, N1, N2, N3, and REM stages of 30 PSG data.
The X-axis represents the normalized feature values and the Y-axis represents the number of epochs. Features are power: (A) 0–30 Hz EEG, (B) 0–30 Hz EMG; power ratio: (C) 0–4 Hz / 0–30 Hz EEG, (D) 8–13 Hz / 0–30 Hz EEG, (E) 22–30 Hz / 0–30 Hz EEG; power (F) 0–4 Hz EOG; spectral frequency: (G) 0–30 Hz mean frequency EEG, (H) 0–30 Hz mean frequency EMG; duration ratio: (I) alpha ratio EEG, (J) Spindle ratio EEG, (K) SWS ratio EEG; amplitude: (L) mean amplitude EMG.
Fig 3
Fig 3. The hypnogram and SWR features of subject no. 1.
The hypnograms scored by gold standard (A) and the automatic staging system (B). The values of features 0–30 E (C), SWS E (D), and 0–4 O (E).
Fig 4
Fig 4. The hypnogram and SCD value of PSG from subject no. 3.
The hypnograms scored by gold standard (A) and the automatic staging system (B). The value of feature SCD (C). The red lines indicate disagreement between the expert and the automatic scoring system.
Fig 5
Fig 5. The hypnogram and SCF values of subject no. 5.
The hypnograms scored by gold standard (A) and the automatic staging system (B). The value of feature SCF (C). The red lines indicate disagreement between the expert and the automatic scoring system.
Fig 6
Fig 6. Reliability analysis examples.
Disagreements can be detected by using the SWR (A), SCD (B) and SCF (C) features.
Fig 7
Fig 7. The architecture of the voting process.
According to the value of the SWR, SCD, and SCF features, the voting process determines a scored epoch as a high reliability or low reliability.
Fig 8
Fig 8. Evaluation of the HCSS system.
(A) The agreement in overall, high-reliability and low-reliability epochs, along with the kappa coefficient between the manual scorings and the HCSS system collaborated scorings. (B) The average of the scoring time for one subject spent in manual and HCSS groups. Percentage of reduced manual scoring time with the assistance of the HCSS system; OA: overall, HR: high-reliability, LR: low-reliability.
Fig 9
Fig 9. Hypnograms of the subject no. 4.
The hypnograms scored by fully manual scoring (scorer 2) (A), fully automatic staging (B) and the HCSS system (C).
See this image and copyright information in PMC

References

    1. Ohayon MM. Epidemiology of insomnia: what we know and what we still need to learn. Sleep medicine reviews. 2002;6(2):97–111. - PubMed
    1. Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus CL, Vaughn BV, et al. The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications, Darien, Illinois, American Academy of Sleep Medicine. 2012;.
    1. Lajnef T, Chaibi S, Ruby P, Aguera PE, Eichenlaub JB, Samet M, et al. Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines. Journal of neuroscience methods. 2015;250:94–105. 10.1016/j.jneumeth.2015.01.022 - DOI - PubMed
    1. Motamedi-Fakhr S, Moshrefi-Torbati M, Hill M, Hill CM, White PR. Signal processing techniques applied to human sleep EEG signals—A review. Biomedical Signal Processing and Control. 2014;10:21–33.
    1. Liang SF, Kuo CE, Hu YH, Cheng YS. A rule-based automatic sleep staging method. Journal of neuroscience methods. 2012;205(1):169–176. 10.1016/j.jneumeth.2011.12.022 - DOI - PubMed

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