Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy. / Olsen, Anders Vinther; Stephansen, Jens; Leary, Eileen; Peppard, Paul E; Sheungshul, Hong; Jennum, Poul Jørgen; Sorensen, Helge; Mignot, Emmanuel.
In: Journal of Neuroscience Methods, Vol. 282, 2017, p. 9-19.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy
AU - Olsen, Anders Vinther
AU - Stephansen, Jens
AU - Leary, Eileen
AU - Peppard, Paul E
AU - Sheungshul, Hong
AU - Jennum, Poul Jørgen
AU - Sorensen, Helge
AU - Mignot, Emmanuel
N1 - Copyright © 2017 Elsevier B.V. All rights reserved.
PY - 2017
Y1 - 2017
N2 - BACKGROUND: Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized that sleep stage dissociations can be objectively detected through the analysis of nocturnal Polysomnography (PSG) data, and that those affecting REM sleep can be used as a diagnostic feature for narcolepsy.NEW METHOD: A Linear Discriminant Analysis (LDA) model using 38 features extracted from EOG, EMG and EEG was used in control subjects to select features differentiating wake, stage N1, N2, N3 and REM sleep. Sleep stage differentiation was next represented in a 2D projection. Features characteristic of sleep stage differences were estimated from the residual sleep stage probability in the 2D space. Using this model we evaluated PSG data from NT1 and non-narcoleptic subjects. An LDA classifier was used to determine the best separation plane.COMPARISON WITH EXISTING METHODS: This method replicates the specificity/sensitivity from the training set to the validation set better than many other methods.RESULTS: Eight prominent features could differentiate narcolepsy and controls in the validation dataset. Using a composite measure and a specificity cut off 95% in the training dataset, sensitivity was 43%. Specificity/sensitivity was 94%/38% in the validation set. Using hypersomnia subjects, specificity/sensitivity was 84%/15%. Analyzing treated narcoleptics the specificity/sensitivity was 94%/10%.CONCLUSION: Sleep stage dissociation can be used for the diagnosis of narcolepsy. However the use of some medications and presence of undiagnosed hypersomnolence patients impacts the result.
AB - BACKGROUND: Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized that sleep stage dissociations can be objectively detected through the analysis of nocturnal Polysomnography (PSG) data, and that those affecting REM sleep can be used as a diagnostic feature for narcolepsy.NEW METHOD: A Linear Discriminant Analysis (LDA) model using 38 features extracted from EOG, EMG and EEG was used in control subjects to select features differentiating wake, stage N1, N2, N3 and REM sleep. Sleep stage differentiation was next represented in a 2D projection. Features characteristic of sleep stage differences were estimated from the residual sleep stage probability in the 2D space. Using this model we evaluated PSG data from NT1 and non-narcoleptic subjects. An LDA classifier was used to determine the best separation plane.COMPARISON WITH EXISTING METHODS: This method replicates the specificity/sensitivity from the training set to the validation set better than many other methods.RESULTS: Eight prominent features could differentiate narcolepsy and controls in the validation dataset. Using a composite measure and a specificity cut off 95% in the training dataset, sensitivity was 43%. Specificity/sensitivity was 94%/38% in the validation set. Using hypersomnia subjects, specificity/sensitivity was 84%/15%. Analyzing treated narcoleptics the specificity/sensitivity was 94%/10%.CONCLUSION: Sleep stage dissociation can be used for the diagnosis of narcolepsy. However the use of some medications and presence of undiagnosed hypersomnolence patients impacts the result.
KW - Adult
KW - Cohort Studies
KW - Discriminant Analysis
KW - Electroencephalography/methods
KW - Electromyography/methods
KW - Electrooculography/methods
KW - Humans
KW - Linear Models
KW - Machine Learning
KW - Middle Aged
KW - Narcolepsy/classification
KW - Polysomnography/methods
KW - Sensitivity and Specificity
KW - Sleep Stages/drug effects
U2 - 10.1016/j.jneumeth.2017.02.004
DO - 10.1016/j.jneumeth.2017.02.004
M3 - Journal article
C2 - 28219726
VL - 282
SP - 9
EP - 19
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
ER -
ID: 197360407