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

Ting Ying Wei, Chung-Ping Young, Yu Ting Liu, Jia Hao Xu, Sheng-Fu Liang, Fu-Zen Shaw, Chin En Kuo

研究成果: Article

摘要

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.

原文English
文章編號92
期刊Biomedical engineering online
18
發行號1
DOIs
出版狀態Published - 2019 九月 4

指紋

Sleep Stages
Rats
Sleep
Research Design
Hyperalgesia
Electroencephalography
Pain
Acids
REM Sleep
Computer Systems
Psychiatry
Rodentia
Consensus
Personnel
Monitoring

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Biomaterials
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

引用此文

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abstract = "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.",
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Development of a rule-based automatic five-sleep-stage scoring method for rats. / Wei, Ting Ying; Young, Chung-Ping; Liu, Yu Ting; Xu, Jia Hao; Liang, Sheng-Fu; Shaw, Fu-Zen; Kuo, Chin En.

於: Biomedical engineering online, 卷 18, 編號 1, 92, 04.09.2019.

研究成果: Article

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T1 - Development of a rule-based automatic five-sleep-stage scoring method for rats

AU - Wei, Ting Ying

AU - Young, Chung-Ping

AU - Liu, Yu Ting

AU - Xu, Jia Hao

AU - Liang, Sheng-Fu

AU - Shaw, Fu-Zen

AU - Kuo, Chin En

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N2 - 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.

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.

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