Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms

Langrock R, Swihart BJ, Caffo BS, Punjabi NM, Crainiceanu CM (2013)
Statistics in Medicine 32(19): 3342-3356.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Langrock, RolandUniBi; Swihart, Bruce J.; Caffo, Brian S.; Punjabi, Naresh M.; Crainiceanu, Ciprian M.
Erscheinungsjahr
2013
Zeitschriftentitel
Statistics in Medicine
Band
32
Ausgabe
19
Seite(n)
3342-3356
ISSN
0277-6715
Page URI
https://pub.uni-bielefeld.de/record/2902622

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Langrock R, Swihart BJ, Caffo BS, Punjabi NM, Crainiceanu CM. Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms. Statistics in Medicine. 2013;32(19):3342-3356.
Langrock, R., Swihart, B. J., Caffo, B. S., Punjabi, N. M., & Crainiceanu, C. M. (2013). Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms. Statistics in Medicine, 32(19), 3342-3356. doi:10.1002/sim.5747
Langrock, R., Swihart, B. J., Caffo, B. S., Punjabi, N. M., and Crainiceanu, C. M. (2013). Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms. Statistics in Medicine 32, 3342-3356.
Langrock, R., et al., 2013. Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms. Statistics in Medicine, 32(19), p 3342-3356.
R. Langrock, et al., “Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms”, Statistics in Medicine, vol. 32, 2013, pp. 3342-3356.
Langrock, R., Swihart, B.J., Caffo, B.S., Punjabi, N.M., Crainiceanu, C.M.: Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms. Statistics in Medicine. 32, 3342-3356 (2013).
Langrock, Roland, Swihart, Bruce J., Caffo, Brian S., Punjabi, Naresh M., and Crainiceanu, Ciprian M. “Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms”. Statistics in Medicine 32.19 (2013): 3342-3356.

8 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Modelling reassurances of clinicians with hidden Markov models.
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PMID: 30626327
Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data.
Huang Q, Cohen D, Komarzynski S, Li XM, Innominato P, Lévi F, Finkenstädt B., J R Soc Interface 15(139), 2018
PMID: 29436510
Nonparametric inference in hidden Markov models using P-splines.
Langrock R, Kneib T, Sohn A, DeRuiter SL., Biometrics 71(2), 2015
PMID: 25586063
Development of the National Healthy Sleep Awareness Project Sleep Health Surveillance Questions.
Morgenthaler TI, Croft JB, Dort LC, Loeding LD, Mullington JM, Thomas SM., J Clin Sleep Med 11(9), 2015
PMID: 26235156

37 References

Daten bereitgestellt von Europe PubMed Central.

Day-night pattern of sudden death in obstructive sleep apnea.
Gami AS, Howard DE, Olson EJ, Somers VK., N. Engl. J. Med. 352(12), 2005
PMID: 15788497
Sleep-disordered breathing and mortality: a prospective cohort study.
Punjabi NM, Caffo BS, Goodwin JL, Gottlieb DJ, Newman AB, O'Connor GT, Rapoport DM, Redline S, Resnick HE, Robbins JA, Shahar E, Unruh ML, Samet JM., PLoS Med. 6(8), 2009
PMID: 19688045
Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep.
Crainiceanu CM, Caffo BS, Di CZ, Punjabi NM., J Am Stat Assoc 104(486), 2009
PMID: 20057925
MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS.
Di CZ, Crainiceanu CM, Caffo BS, Punjabi NM., Ann Appl Stat 3(1), 2009
PMID: 20221415
Power Spectral Analysis of EEG Activity During Sleep in Cigarette Smokers
Zhang L, Samet JM, Caffo BS, Bankman I, Punjabi NM., 2008

Cappé O, Moulines ER, Rydén T., 2005

Zucchini W, MacDonald IL., 2009
A nonparametric multiplicative hazard model for event history analysis
Fahrmeir L, Klinger A., 1998
Modelling and exploring human sleep with event history analysis.
Yassouridis A, Steiger A, Klinger A, Fahrmeir L., J Sleep Res 8(1), 1999
PMID: 10188133
Modeling hypersomnolence in sleep-disordered breathing. A novel approach using survival analysis.
Punjabi NM, O'hearn DJ, Neubauer DN, Nieto FJ, Schwartz AR, Smith PL, Bandeen-Roche K., Am. J. Respir. Crit. Care Med. 159(6), 1999
PMID: 10351907
Characterization of sleep stages by correlations in the magnitude and sign of heartbeat increments
Kantelhardt JW, Ashkenazy Y, Ivanov PC, Bunde A, Havlin S, Penzel T, Peter JH, Stanley HE., 2002
The association between daytime sleepiness and sleep-disordered breathing in NREM and REM sleep.
Punjabi NM, Bandeen-Roche K, Marx JJ, Neubauer DN, Smith PL, Schwartz AR., Sleep 25(3), 2002
PMID: 12003161
Sleep continuity measured by survival curve analysis.
Norman RG, Scott MA, Ayappa I, Walsleben JA, Rapoport DM., Sleep 29(12), 2006
PMID: 17252894
Geoadditive survival models
Hennerfeind A, Brezger A, Fahrmeir L., 2006
Bayesian Semiparametric Multi-State Models
Kneib T, Hennerfeind A., 2008
Characterizing sleep structure using the hypnogram.
Swihart BJ, Caffo B, Bandeen-Roche K, Punjabi NM., J Clin Sleep Med 4(4), 2008
PMID: 18763427
An overview of observational sleep research with application to sleep stage transitioning.
Caffo B, Swihart B, Laffan A, Crainiceanu C, Punjabi N., Chance (N Y) 22(1), 2009
PMID: 20046532
Utility of sleep stage transitions in assessing sleep continuity.
Laffan A, Caffo B, Swihart BJ, Punjabi NM., Sleep 33(12), 2010
PMID: 21120130

Swihart BJ, Caffo BS, Crainiceanu CM., 2010
HMMs and coupled HMMs for multi-channel EEG classification
Zhong S, Ghosh J., 2002
Gaussian Observation Hidden Markov Models for EEG Analysis
Penny W, Roberts S., 1998
A reliable probabilistic sleep stager based on a single EEG signal.
Flexer A, Gruber G, Dorffner G., Artif Intell Med 33(3), 2005
PMID: 15811785
Mixed hidden Markov models: An extension of the hidden Markov model to the longitudinal data setting
Altman R., 2007

MacDonald IL, Zucchini W., 1997
Analysis of longitudinal data of epileptic seizure: a two state hidden Markov approach
Wang P, Puterman ML., 2001
Latent Markov model for longitudinal binary data: An application to the performance evaluation of nursing homes
Bartolucci F, Lupparelli M, Montanari GE., 2009
Case Studies in Bayesian Statistics
Seltman HJ., 2002
Modeling time series of animal behavior by means of a latent-state model with feedback.
Zucchini W, Raubenheimer D, MacDonald IL., Biometrics 64(3), 2007
PMID: 18047533
On the application of mixed hidden Markov models to multiple behavioural time series.
Schliehe-Diecks S, Kappeler PM, Langrock R., Interface Focus 2(2), 2012
PMID: 23565332
The Sleep Heart Health study: Design, rationale, and methods
Quan SE, Howard TV, Iber C, Kiley JP, Nieto FJ, O’Connor GT, Rapoport DM, Redline S, Robbins J, Samet JM., 1997
The central role of the propensity score in observational studies for causal effects
Rosenbaum PR, Rubin DB., 1983
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
Ho DE, Imai K, King G, Stuart EA., 2011
Latent Dirichlet allocation
Blei DM, Ng AY, Jordan MI., 2003
A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects
Rechtschaffen A, Kales A., 1968
Hidden Markov models with arbitrary dwell-time distributions
Langrock R, Zucchini W., 2011
A semiparametric approach to hidden Markov models under longitudinal observations
Maruotti A, Rydén T., 2009

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