Development of a rule-based automatic five-sleep-stage scoring method for rats.

Background: Sleep problem or disturbance often exists in pain or neurological/psychiatric diseases. However, sleep scoring is a time-consuming tedious labor. Very few studies discuss the 5-stage (wake/NREM1/NREM2/transition sleep/REM) automatic fine analysis of wake-sleep stages in rodent models. Th...

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Publicado en:BioMedical Engineering OnLine Vol. 18; no. 1
Autores principales: Wei, Ting-Ying, Young, Chung-Ping, Liu, Yu-Ting, Xu, Jia-Hao, Liang, Sheng-Fu, Shaw, Fu-Zen, Kuo, Chin-En
Formato: equations & formulas research tables/charts Journal Article
Publicado: BioMed Central 9/4/2019
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 9/4/2019
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      pub: BioMed Central
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        10.1186/s12938-019-0712-8
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        138430604
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        atl: Development of a rule-based automatic five-sleep-stage scoring method for rats.
      aug:
        au:
          Wei, Ting-Ying
          Young, Chung-Ping
          Liu, Yu-Ting
          Xu, Jia-Hao
          Liang, Sheng-Fu
          Shaw, Fu-Zen
          Kuo, Chin-En
        affil: Dept. of Computer Science and Information Engineering, National Cheng Kung University, 701, Tainan, Taiwan
      sug:
        subj:
          Sleep Stages
          Signal Processing, Computer Assisted
          Wakefulness
          Automation
          Animals
          Rats
          Polysomnography
          Electroencephalography
          Hyperalgesia Physiopathology
      ab: Background: Sleep problem or disturbance often exists in pain or neurological/psychiatric diseases. However, sleep scoring is a time-consuming tedious labor. Very few studies discuss the 5-stage (wake/NREM1/NREM2/transition sleep/REM) automatic fine analysis of wake-sleep stages in rodent models. The present study aimed to develop and validate an automatic rule-based classification of 5-stage wake-sleep pattern in acid-induced widespread hyperalgesia model of the rat.Results: The overall agreement between two experts' consensus and automatic scoring in the 5-stage and 3-stage analyses were 92.32% (κ = 0.88) and 94.97% (κ = 0.91), respectively. Standard deviation of the accuracy among all rats was only 2.93%. Both frontal-occipital EEG and parietal EEG data showed comparable accuracies. The results demonstrated the performance of the proposed method with high accuracy and reliability. Subtle changes exhibited in the 5-stage wake-sleep analysis but not in the 3-stage analysis during hyperalgesia development of the acid-induced pain model. Compared with existing methods, our method can automatically classify vigilance states into 5-stage or 3-stage wake-sleep pattern with a promising high agreement with sleep experts.Conclusions: In this study, we have performed and validated a reliable automated sleep scoring system in rats. The classification algorithm is less computation power, a high robustness, and consistency of results. The algorithm can be implanted into a versatile wireless portable monitoring system for real-time analysis in the future.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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