Sleep · 2003
An automated pontine-wave detection system.
СтатьяПоддержка службы здравоохранения СШАНазвание и аннотация: Перевод пока не подготовлен
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Li Chao, Radulovacki Miodrag, Carley David W
Аннотация
STUDY OBJECTIVES: Pontine-waves (P-waves), the pontine component of ponto-geniculo-occipital waves, represent a close marker of brainstem phasic events and are associated with cardiorespiratory changes in sleep. Because visual scoring is subjective and cumbersome, we developed an automated P-wave analysis system, which could detect and classify P-waves as clusters or as isolated events in bipolar recordings of the pontine electroencephalogram (EEG) of conscious rats.
DESIGN: A computer algorithm was developed to extract and normalize each half-wave of the pontine EEG according to the background noise level. Candidate events for different P-wave patterns (uniphasic, biphasic, and triphasic) were compared to a corresponding set of amplitude and duration thresholds to identify P-waves and to reject artifacts.
PARTICIPANTS: Ten adult male Sprague-Dawley rats were instrumented for chronic polysomnography.
MEASUREMENTS AND RESULTS: Two human experts manually scored each recording, and their consensus score was used as the "gold standard" for algorithm optimization and validation. The algorithm's scoring thresholds were optimized on a training set of 5 six-hour polysomnographic records, yielding 96.8% accuracy and 97.7% sensitivity versus human consensus scoring. Validation of the algorithm, using the optimized threshold values, was conducted using a set of 5 independent recordings, resulting in 94.8% accuracy and 94.7% sensitivity versus human consensus scoring.
CONCLUSIONS: We have developed and validated an automated system for detection and classification of P-waves in conscious rats with advantages over human scoring, including increased speed and perfect reliability.
DESIGN: A computer algorithm was developed to extract and normalize each half-wave of the pontine EEG according to the background noise level. Candidate events for different P-wave patterns (uniphasic, biphasic, and triphasic) were compared to a corresponding set of amplitude and duration thresholds to identify P-waves and to reject artifacts.
PARTICIPANTS: Ten adult male Sprague-Dawley rats were instrumented for chronic polysomnography.
MEASUREMENTS AND RESULTS: Two human experts manually scored each recording, and their consensus score was used as the "gold standard" for algorithm optimization and validation. The algorithm's scoring thresholds were optimized on a training set of 5 six-hour polysomnographic records, yielding 96.8% accuracy and 97.7% sensitivity versus human consensus scoring. Validation of the algorithm, using the optimized threshold values, was conducted using a set of 5 independent recordings, resulting in 94.8% accuracy and 94.7% sensitivity versus human consensus scoring.
CONCLUSIONS: We have developed and validated an automated system for detection and classification of P-waves in conscious rats with advantages over human scoring, including increased speed and perfect reliability.