Computer Simulation of Sleep EEG Patterns with a Markov Chain Model

Zung, William W. K.; Naylor, Thomas H.; Gianturco, Daniel T.; Wilson, Wilkie A.

doi:10.1007/978-1-4899-7313-9_36

Cited by 18 publications

(22 citation statements)

References 6 publications

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“…This would be an interesting topic for future research. With regard to model structure, the choice of HMMs was motivated by the fact that sleep dynamics are reasonably well-represented by Markov chain models (Zung et al 1966), and the HMM’s key parameters, the transition probabilities, convey information about the dynamics and stability of the underlying states. It is possible that other more powerful machine learning techniques may provide interesting insights into piezo signal dynamics in relation to overt and subtle behaviors.…”

Section: Discussionmentioning

confidence: 99%

“…In keeping with the polyphasic nature of their activity cycles, mice spent variable amounts of time in each of the three states described above, and made rapid transitions between them at irregular intervals. The seemingly random nature of these transitions between discrete states suggested that their dynamics could be modeled as a Markov chain (Zung et al 1966), in which a system occupies one of many discrete states at any instant but makes random transitions to other states in a manner determined only by the current state (the Markov property). A Markov chain in which the true state is concealed but characterized by observations whose probability distribution is conditioned on the state is known as a hidden Markov model or HMM.…”

Section: Methodsmentioning

confidence: 99%

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Noninvasive dissection of mouse sleep using a piezoelectric motion sensor

Yaghouby

Donohue

O’Hara

et al. 2016

Journal of Neuroscience Methods

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Background Changes in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage-floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor. New Method Vigilance state was scored manually in 4-second epochs for 24-hour EEG/EMG recordings in twenty mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing respectively. These states were hypothesized to correspond to Wake, NREM, and REM respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis. Results Using only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51%) and high specificity (96%). The classifier sometimes confused light NREM sleep—characterized by irregular breathing and moderate delta EEG power—with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM respectively. Comparison with Existing Methods Unlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM. Conclusions This approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Noninvasive dissection of mouse sleep using a piezoelectric motion sensor

Yaghouby

Donohue

O’Hara

et al. 2016

Journal of Neuroscience Methods

View full text Add to dashboard Cite

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“…The HMM is one such modeling technique that maps continuous observations onto discrete hidden states [15]. Early statistical models of sleep dynamics used Markov chain models to represent probabilistic transitions between stages of sleep extracted from expert-scored hypnograms [28]. These models have become more refined and are being used to characterize disordered sleep and the effect of medication [29, 30].…”

Section: Discussionmentioning

confidence: 99%

Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables

Yaghouby

Sunderam

2015

Computers in Biology and Medicine

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The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18 to 79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models—specifically Gaussian mixtures and hidden Markov models—are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's K statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations.

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“…[26][27][28][29] Approaches ranged from simply calculating average transition probabilities over constant periods of the night 29 to complex mixed effects models. 26 Yassouridis and others 30 (Figure 2A), transition probabilities to wake, S2, and REM are higher and transition probabilities to S1, S3, and S4 are lower.…”

Section: Discussionmentioning

confidence: 99%

“…All data were integrated into a single model, as opposed to approaches where several models were built for this purpose (e.g., Karlsson and others 26 ). In addition, it was not necessary to divide the sleep period time into segments with constant transition probabilities (e.g., Zung and others 29 and Kemp and Kamphuisen 27 ). In autoregressive models, realizations of past and present states are explicitly used to predict future states.…”

Section: Discussionmentioning

confidence: 99%