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An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters.

Rempe MJ, Clegern WC, Wisor JP - Nat Sci Sleep (2015)

Bottom Line: Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies.

View Article: PubMed Central - PubMed

Affiliation: Mathematics and Computer Science, Whitworth University, Spokane, WA, USA ; College of Medical Sciences and Sleep and Performance Research Center, Washington State University, Spokane, WA, USA.

ABSTRACT

Introduction: Rodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2-10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.

Methods: We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration.

Results: More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.

Conclusions: Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies. Automated scoring is an efficient alternative to visual inspection in studies of strain differences in sleep and the temporal dynamics of sleep-related physiological parameters.

No MeSH data available.


Related in: MedlinePlus

Strain differences in sleep/wake state timing scored in 10-second epochs.Notes: Strain differences in the time course of sleep/wake states were observed in human-scored data (A, C and E) or machine-scored data (B, D and F). Filled circles represent data from the BA strain, and open circles represent data from the B6 strain. Asterisks indicate 60-minute epochs in which there is a statistically significant difference between strains (Fisher’s protected least-significant difference). Timing of the dark and light phases of the 12:12 cycle is indicated by the black and white bars at the top of each graph.Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; REMS, rapid-eye-movement sleep; SWS, slow-wave sleep.
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f10-nss-7-085: Strain differences in sleep/wake state timing scored in 10-second epochs.Notes: Strain differences in the time course of sleep/wake states were observed in human-scored data (A, C and E) or machine-scored data (B, D and F). Filled circles represent data from the BA strain, and open circles represent data from the B6 strain. Asterisks indicate 60-minute epochs in which there is a statistically significant difference between strains (Fisher’s protected least-significant difference). Timing of the dark and light phases of the 12:12 cycle is indicated by the black and white bars at the top of each graph.Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; REMS, rapid-eye-movement sleep; SWS, slow-wave sleep.

Mentions: Posthoc comparisons across strains yielded strikingly similar time-of-day–specific effects of strain on sleep timing in the human-scored and machine-scored datasets (Figure 10). For instance, SWS as a percentage of time differed significantly between strains (Fisher’s protected least-significant difference) in hours 5, 9, 10, 19, and 24, and at no other times, regardless of whether states were scored by human or machine. Wake time differed significantly between strains in hours 5, 9, 10, 19, and 24, regardless of whether states were scored by human or machine. REMS time differed significantly between strains in hours 9, 11–13, and 19–21 of recording, regardless of whether states were scored by human or machine. Thus, although machine-based scoring exhibited a significant bias toward SWS, it nonetheless detected the experimental effect of strain on sleep timing. This analysis was not repeated for the 2-second epoch data, as those 8,640 epochs represented a 5-hour window within which daily rhythms of sleep and wake are not apparent.


An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters.

Rempe MJ, Clegern WC, Wisor JP - Nat Sci Sleep (2015)

Strain differences in sleep/wake state timing scored in 10-second epochs.Notes: Strain differences in the time course of sleep/wake states were observed in human-scored data (A, C and E) or machine-scored data (B, D and F). Filled circles represent data from the BA strain, and open circles represent data from the B6 strain. Asterisks indicate 60-minute epochs in which there is a statistically significant difference between strains (Fisher’s protected least-significant difference). Timing of the dark and light phases of the 12:12 cycle is indicated by the black and white bars at the top of each graph.Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; REMS, rapid-eye-movement sleep; SWS, slow-wave sleep.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4562753&req=5

f10-nss-7-085: Strain differences in sleep/wake state timing scored in 10-second epochs.Notes: Strain differences in the time course of sleep/wake states were observed in human-scored data (A, C and E) or machine-scored data (B, D and F). Filled circles represent data from the BA strain, and open circles represent data from the B6 strain. Asterisks indicate 60-minute epochs in which there is a statistically significant difference between strains (Fisher’s protected least-significant difference). Timing of the dark and light phases of the 12:12 cycle is indicated by the black and white bars at the top of each graph.Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; REMS, rapid-eye-movement sleep; SWS, slow-wave sleep.
Mentions: Posthoc comparisons across strains yielded strikingly similar time-of-day–specific effects of strain on sleep timing in the human-scored and machine-scored datasets (Figure 10). For instance, SWS as a percentage of time differed significantly between strains (Fisher’s protected least-significant difference) in hours 5, 9, 10, 19, and 24, and at no other times, regardless of whether states were scored by human or machine. Wake time differed significantly between strains in hours 5, 9, 10, 19, and 24, regardless of whether states were scored by human or machine. REMS time differed significantly between strains in hours 9, 11–13, and 19–21 of recording, regardless of whether states were scored by human or machine. Thus, although machine-based scoring exhibited a significant bias toward SWS, it nonetheless detected the experimental effect of strain on sleep timing. This analysis was not repeated for the 2-second epoch data, as those 8,640 epochs represented a 5-hour window within which daily rhythms of sleep and wake are not apparent.

Bottom Line: Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies.

View Article: PubMed Central - PubMed

Affiliation: Mathematics and Computer Science, Whitworth University, Spokane, WA, USA ; College of Medical Sciences and Sleep and Performance Research Center, Washington State University, Spokane, WA, USA.

ABSTRACT

Introduction: Rodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2-10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.

Methods: We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration.

Results: More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.

Conclusions: Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies. Automated scoring is an efficient alternative to visual inspection in studies of strain differences in sleep and the temporal dynamics of sleep-related physiological parameters.

No MeSH data available.


Related in: MedlinePlus