Representations of temporal sleep dynamics: Review and synthesis of the literature

Hermans, Lieke W A; Huijben, Iris A. M.; Gorp, Hans van; Leufkens, Tim; Fonseca, Pedro; Overeem, Sebastiaan; Gilst, Merel M van

doi:10.1016/j.smrv.2022.101611

Cited by 16 publications

(5 citation statements)

References 85 publications

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“…An additional insight we have gained from plotting the sleep features (Figure 2b) is that sleep stages don't seem to be discretely separated, but rather lie on a continuous spectrum. Recent works have advocated modelling sleep as a continuous process [33,34,35], which may be a more biologically accurate representation. Hence, uncertainty estimation methods can inform the medical field on how to best describe and define sleep.…”

Section: Discussionmentioning

confidence: 99%

Feature matching as improved transfer learning technique for wearable EEG

Heremans

Phan

Ansari

et al. 2022

Biomedical Signal Processing and Control

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Section: Discussionmentioning

confidence: 99%

Feature matching as improved transfer learning technique for wearable EEG

Heremans

Phan

Ansari

et al. 2022

Biomedical Signal Processing and Control

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“…However, alternative sleep representations have also been proposed. For example, the odds ratio product [28], data-driven clusters, or continuous representations (see Hermans et al [29] for an overview). These alternative sleep staging methods are promising in the regard that they might prove to be better representations of the structure of sleep and provide physicians with more information.…”

Section: Discussionmentioning

confidence: 99%

Certainty about uncertainty in sleep staging: a theoretical framework

Gorp

Huijben

Fonseca

et al. 2022

Sleep

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Sleep stage classification is an important tool for the diagnosis of sleep disorders. Because sleep staging has such a high impact on clinical outcome, it is important that it is done reliably. However, it is known that uncertainty exists in both expert scorers and automated models. On average, agreement between human scorers is only 82.6%. In this manuscript, we provide a theoretical framework to facilitate discussion and further analyses of uncertainty in sleep staging. To this end, we introduce two variants of uncertainty, known from statistics and the machine learning community: aleatoric and epistemic uncertainty. We discuss what these types of uncertainties are, why the distinction is useful, where they arise from in sleep staging, and provide recommendations on how this framework can improve sleep staging in the future.

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“…Preprocessing: For the input to this model the data was first decomposed in sub-bands using the Fourier Decomposition Method (FDM) [28]. The following frequency bands were extracted: [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] Hz, [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz, [8][9][10][11][12] Hz, [4][5][6][7][8] Hz, [0,4] Hz, [0.5-2] Hz, [2][3][4][5]…”

Section: Architecture Of the Scoring Methodologymentioning

confidence: 99%

Automatic Sleep Stage Classification with Optimized Selection of EEG Channels

Stenwig

Soler

Furuki

et al. 2022

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

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Visual inspection of Polysomnography (PSG) recordings by sleep experts, based on established guidelines, has been the gold standard in sleep stage classification. This approach is expensive, time-consuming, and mostly limited to experimental research and clinical cases of major sleep disorders. Various automatic approaches to sleep scoring have been emerging in the past years and are opening the way to a quick computational assessment of sleep architecture that may find its way to the clinics. With the hope to make sleep scoring a fully automated process in the clinics, we report here an ensemble algorithm that aims at not only predicting sleep stages but of doing so with an optimized minimal number of EEG channels. For that, we combine a genetic algorithm-based optimization with a classification framework that minimizes the number of channels used by the machine learning algorithm to quantify sleep stages. This resulted in a sleep scoring with an F1 score of 0.793 for the fully automated model and 0.82 for the model trained on 10 percent of the unseen subject, both with only 3 EEG channels. The ensemble algorithm is based on a combination of extremely randomized trees and MiniRocket classifiers. The algorithm was trained, validated, and tested on night sleep PSG data collected from 7 subjects. Our approach's novelty lies in using the minimum information needed for automated sleep scoring, based on a systematic search that concurrently selects the optimal-minimum number of EEG channels and the best-performing features for the machine learning classifier. The optimization framework presented in this work may enable new flexible designs for sleep scoring devices suited to studies in the comfort of homes, easily and inexpensively. In this way facilitate experimental and clinical studies in large populations.

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Representations of temporal sleep dynamics: Review and synthesis of the literature

Cited by 16 publications

References 85 publications

Feature matching as improved transfer learning technique for wearable EEG

Feature matching as improved transfer learning technique for wearable EEG

Certainty about uncertainty in sleep staging: a theoretical framework

Automatic Sleep Stage Classification with Optimized Selection of EEG Channels

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