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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

Homeostatic modeling of lactate data and SWA scored by human or machine.Notes: The left column shows human-scored data and the right-hand column shows machine-scored data. The top four panels show scaled lactate data in 10-second epochs scored by a human (A), 10-second epochs autoscored (B), 2-second epochs scored by a human (C), and 2-second epochs autoscored (D). The bottom four panels show SWA in 5-minute sleep episodes using 10-second epochs scored by a human (E), 10-second epochs autoscored (F), 2-second epochs scored by a human (G), and 2-second epochs autoscored (H). In each panel, a homeostatic model for lactate or SWA was fit to the data (solid curve). The difference in time scale between the lower panels and the upper panels reflects the difference in temporal dynamics of SWA versus those of lactate.Abbreviations: REMS, rapid-eye-movement sleep; SWA, slow-wave activity; SWS, slow-wave sleep.
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f7-nss-7-085: Homeostatic modeling of lactate data and SWA scored by human or machine.Notes: The left column shows human-scored data and the right-hand column shows machine-scored data. The top four panels show scaled lactate data in 10-second epochs scored by a human (A), 10-second epochs autoscored (B), 2-second epochs scored by a human (C), and 2-second epochs autoscored (D). The bottom four panels show SWA in 5-minute sleep episodes using 10-second epochs scored by a human (E), 10-second epochs autoscored (F), 2-second epochs scored by a human (G), and 2-second epochs autoscored (H). In each panel, a homeostatic model for lactate or SWA was fit to the data (solid curve). The difference in time scale between the lower panels and the upper panels reflects the difference in temporal dynamics of SWA versus those of lactate.Abbreviations: REMS, rapid-eye-movement sleep; SWA, slow-wave activity; SWS, slow-wave sleep.

Mentions: To further investigate the utility of the machine scoring, we employed a homeostatic model to predict the state-dependent dynamics of SWA and cerebral lactate concentration.22 We applied this model to both the human-scored and machine-scored data using 10-second epochs and 2-second epochs (Figure 7). The temporal dynamics of the optimized model were unchanged with respect to scoring method using both 10-second epochs (Figure 7A, B, E, and F) or 2-second epochs (Figure 7C, D, G, and H). Because only 8,640 of the 2-second epochs were scored by a human, only those data are shown in panels 7A–7D. This corresponds to 4.8 hours of data starting at 10 am. The dynamics of the lactate signal were minimally affected by the choice of 2-second or 10-second epoch length. In panels 7E–7H, the homeostatic model was fit to delta power (1–4 Hz) in 5-minute episodes made up of at least 90% SWS. Although the model of delta power also used 8,640 epochs as training for the machine scoring, all of the data are shown since the temporal dynamics of SWA are slower than those of lactate.


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)

Homeostatic modeling of lactate data and SWA scored by human or machine.Notes: The left column shows human-scored data and the right-hand column shows machine-scored data. The top four panels show scaled lactate data in 10-second epochs scored by a human (A), 10-second epochs autoscored (B), 2-second epochs scored by a human (C), and 2-second epochs autoscored (D). The bottom four panels show SWA in 5-minute sleep episodes using 10-second epochs scored by a human (E), 10-second epochs autoscored (F), 2-second epochs scored by a human (G), and 2-second epochs autoscored (H). In each panel, a homeostatic model for lactate or SWA was fit to the data (solid curve). The difference in time scale between the lower panels and the upper panels reflects the difference in temporal dynamics of SWA versus those of lactate.Abbreviations: REMS, rapid-eye-movement sleep; SWA, slow-wave activity; SWS, slow-wave sleep.
© Copyright Policy
Related In: Results  -  Collection

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

f7-nss-7-085: Homeostatic modeling of lactate data and SWA scored by human or machine.Notes: The left column shows human-scored data and the right-hand column shows machine-scored data. The top four panels show scaled lactate data in 10-second epochs scored by a human (A), 10-second epochs autoscored (B), 2-second epochs scored by a human (C), and 2-second epochs autoscored (D). The bottom four panels show SWA in 5-minute sleep episodes using 10-second epochs scored by a human (E), 10-second epochs autoscored (F), 2-second epochs scored by a human (G), and 2-second epochs autoscored (H). In each panel, a homeostatic model for lactate or SWA was fit to the data (solid curve). The difference in time scale between the lower panels and the upper panels reflects the difference in temporal dynamics of SWA versus those of lactate.Abbreviations: REMS, rapid-eye-movement sleep; SWA, slow-wave activity; SWS, slow-wave sleep.
Mentions: To further investigate the utility of the machine scoring, we employed a homeostatic model to predict the state-dependent dynamics of SWA and cerebral lactate concentration.22 We applied this model to both the human-scored and machine-scored data using 10-second epochs and 2-second epochs (Figure 7). The temporal dynamics of the optimized model were unchanged with respect to scoring method using both 10-second epochs (Figure 7A, B, E, and F) or 2-second epochs (Figure 7C, D, G, and H). Because only 8,640 of the 2-second epochs were scored by a human, only those data are shown in panels 7A–7D. This corresponds to 4.8 hours of data starting at 10 am. The dynamics of the lactate signal were minimally affected by the choice of 2-second or 10-second epoch length. In panels 7E–7H, the homeostatic model was fit to delta power (1–4 Hz) in 5-minute episodes made up of at least 90% SWS. Although the model of delta power also used 8,640 epochs as training for the machine scoring, all of the data are shown since the temporal dynamics of SWA are slower than those of lactate.

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