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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

Differences in residuals of model fit to data scored by human or machine.Notes: Data represent the residuals associated with optimized time constants for modeling SWA (A and B) and lactate (C and D) from human-scored data (black bars) and machine-scored data (gray bars) in either 10-second or 2-second epochs. P-values are shown for main effect of scoring method on time constants. Asterisk denotes significant difference for human versus machine scoring within the BA strain (Fisher’s protected least-significant difference).Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; NS, not significant; R SWA, slow-wave activity.
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f9-nss-7-085: Differences in residuals of model fit to data scored by human or machine.Notes: Data represent the residuals associated with optimized time constants for modeling SWA (A and B) and lactate (C and D) from human-scored data (black bars) and machine-scored data (gray bars) in either 10-second or 2-second epochs. P-values are shown for main effect of scoring method on time constants. Asterisk denotes significant difference for human versus machine scoring within the BA strain (Fisher’s protected least-significant difference).Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; NS, not significant; R SWA, slow-wave activity.

Mentions: Residuals (Figure 9), a measure of the deviation of optimized mathematical modeling values from the data being modeled, provide an indication of the performance of the model. Lower residuals indicate a better fit to the data being modeled. Two-way analysis of variance with strain as a between-subjects variable and scoring method (automated versus manual) as a within-subjects variable indicated significant effects of scoring method on residual values for modeling lactate dynamics in 10-second epochs (Figure 9C; F1,20=5.3, P=0.032) and SWA dynamics in 2-second epochs (Figure 9B; F1,20=5.3, P=0.032). Scoring method did not significantly affect residuals for lactate dynamics in 2-second epochs (Figure 9D) or SWA dynamics in 10-second epochs (Figure 9A). Residuals did not vary as a function of strain in any of the four analyses, nor were scoring method × strain interactions significant. Where residuals were affected by scoring method (Figure 9B and C), the residuals were lower (albeit modestly) for automated scoring than for manual scoring. Thus, the performance of automated scoring is as effective as, if not modestly more effective than, manual scoring, for use in modeling sleep state-dependent dynamics of physiological variables.


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)

Differences in residuals of model fit to data scored by human or machine.Notes: Data represent the residuals associated with optimized time constants for modeling SWA (A and B) and lactate (C and D) from human-scored data (black bars) and machine-scored data (gray bars) in either 10-second or 2-second epochs. P-values are shown for main effect of scoring method on time constants. Asterisk denotes significant difference for human versus machine scoring within the BA strain (Fisher’s protected least-significant difference).Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; NS, not significant; R SWA, slow-wave activity.
© Copyright Policy
Related In: Results  -  Collection

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

f9-nss-7-085: Differences in residuals of model fit to data scored by human or machine.Notes: Data represent the residuals associated with optimized time constants for modeling SWA (A and B) and lactate (C and D) from human-scored data (black bars) and machine-scored data (gray bars) in either 10-second or 2-second epochs. P-values are shown for main effect of scoring method on time constants. Asterisk denotes significant difference for human versus machine scoring within the BA strain (Fisher’s protected least-significant difference).Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; NS, not significant; R SWA, slow-wave activity.
Mentions: Residuals (Figure 9), a measure of the deviation of optimized mathematical modeling values from the data being modeled, provide an indication of the performance of the model. Lower residuals indicate a better fit to the data being modeled. Two-way analysis of variance with strain as a between-subjects variable and scoring method (automated versus manual) as a within-subjects variable indicated significant effects of scoring method on residual values for modeling lactate dynamics in 10-second epochs (Figure 9C; F1,20=5.3, P=0.032) and SWA dynamics in 2-second epochs (Figure 9B; F1,20=5.3, P=0.032). Scoring method did not significantly affect residuals for lactate dynamics in 2-second epochs (Figure 9D) or SWA dynamics in 10-second epochs (Figure 9A). Residuals did not vary as a function of strain in any of the four analyses, nor were scoring method × strain interactions significant. Where residuals were affected by scoring method (Figure 9B and C), the residuals were lower (albeit modestly) for automated scoring than for manual scoring. Thus, the performance of automated scoring is as effective as, if not modestly more effective than, manual scoring, for use in modeling sleep state-dependent dynamics of physiological variables.

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