A comparison of probabilistic classifiers for sleep stage classification

Fonseca, Pedro; Teuling, Niek Den; Long, Xi; Aarts, Ronald M.

doi:10.1088/1361-6579/aabbc2

Cited by 35 publications

(28 citation statements)

References 37 publications

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“…This leads to the development of automatic sleep stage classification. Unfortunately, conventional algorithms utilizing heart rate features did not have a high accuracy for five-stage sleep stage classification [ 28 , 29 , 30 ]. Some studies tried combine sleep stages to simplify the problem.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, Wei et al [ 28 ] and Radha et al [ 29 ] proposed a deep learning approach for sleep stage classification, utilizing heart rate features. The authors of [ 30 ] compare different machine learning models utilizing ECG features. Studies tend to group some sleep stages together to reduce the problems, yet still fail to produce accurate classifications.…”

Section: Introductionmentioning

confidence: 99%

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Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

Mitsukura

Sumali

Nagura

et al. 2020

Sensors

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Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors’ noise sensitivity. In this paper, a robust method for sleep time BCG measurement and a mathematical model for predicting sleep stages using BCG are described. The novel BCG measurement algorithm can be described in three steps: preprocessing, creation of heartbeat signal template, and template matching for heart rate variability detection. The effectiveness of this algorithm was validated with 99 datasets from 36 subjects, with photoplethysmography (PPG) to compute ground truth heart rate variability (HRV). On average, 86.9% of the inter-beat intervals were detected and the mean error was 8.5ms. This shows that our method successfully extracted beat-to-beat intervals from BCG during sleep, making its usability comparable to those of clinical ECGs. Consequently, compared to other conventional BCG systems, even more accurate sleep heart rate monitoring with a smaller burden to the patient is available. Moreover, the accuracy of the sleep stages mathematical model, validated with 100 datasets from 25 subjects, is 80%, which is higher than conventional five-stage sleep classification algorithms (max: 69%). Although, in this paper, we applied the mathematical model to heart rate interval features from BCG, theoretically, this sleep stage prediction algorithm can also be applied to ECG-extracted heart rate intervals.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

Mitsukura

Sumali

Nagura

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…To find the unlabeled training instances that correspond to potential transitions between sleep stages, we propose to use HMMs that are popular in EEG and PSG signals processing [16]. Recently, HMMs were used in different manners for sleep EEG artefact detection [17], sleep stage classification [18] or post-hoc refinement of classification results [19].…”

Section: Active Learning and Ambiguous Instancesmentioning

confidence: 99%

Semi-Automated Sleep EEG Scoring with Active Learning and HMM-Based Deletion of Ambiguous Instances

Macaš¹,

Grimová²,

Gerla³

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Sleep scoring is an important tool for physicians. Assigning of segments of long biomedical signal into sleep stages is, however, a very time consuming, tedious and expensive task which is performed by an expert. Automatic sleep scoring is not well accepted in clinical practice because of low interactivity and unacceptable error, which is often caused by inter-patient variability. This is solved by proposing a semi-automatic approach, where parts of the signal are selected for manual labeling by active learning and the resulting classifier is used for automatic labeling of the remaining signal. The active learning is disturbed by noisy ambiguous data instances caused by continuous character of the sleep stage transitions and a removal of such transitional instances from the training set prior to active learning can improve the efficiency of the method. This paper proposes to use the hidden Markov model for the detection of the transitional instances. It shows experimentally on 35 sleep EEG recordings that such a method significantly improves the semi-automatic method. A complete methodology for semi-automatic sleep scoring is proposed and evaluated, which can be better accepted as a decision support tool for sleep scoring experts.

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“…Fonseca et al . () found that considering a time variable in addition to heart rate signals could detect sleep stages better than heart‐rate signals alone. Specifically, time and heart rate variables were discretised using K ‐means clustering, which were then used as observation sequences in HMMs and conditional random fields.…”

Section: Introductionmentioning

confidence: 99%

Using hidden Markov models with raw, triaxial wrist accelerometry data to determine sleep stages

Trevenen

Turlach

Eastwood

et al. 2019

Aus NZ J of Statistics

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Accelerometry is a low-cost and noninvasive method that has been used to discriminate sleep from wake, however, its utility to detect sleep stages is unclear. We detail the development and comparison of methods which utilise raw, triaxial accelerometry data to classify varying stages of sleep, ranging from sleep/wake detection to discriminating rapid eye movement sleep, stage one sleep, stage two sleep, deep sleep and wake. First-and second-order hidden Markov models (HMMs) with time-homogeneous and time-varying transition probability matrices, along with continuous acceleration observations in the form of a Gaussianobservation HMM and K-means classified acceleration in a discrete-observation HMM were explored. In addition, generalised linear mixed models (GLMMs) with binary and multinomial responses and logit link functions were considered as was whether incorporating adjoining acceleration information into the models improved prediction. Model predictions were compared to the reference-standard in sleep detection (polysomnography) and outcome accuracies were calculated. Consistently, HMMs yielded greater sleep stage detection than GLMMs but there was little difference between first-and second-order HMMs. Varying degrees of difference were observed when comparing Gaussian-observation HMMs to discrete-observation HMMs, and time-varying HMMs yielded greater discrimination than time-homogeneous HMMs, as did models which considered adjoining acceleration information. These results suggest that wrist-worn accelerometry data may be able to detect sleep stages but that further investigation is required to optimise classification accuracy.

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A comparison of probabilistic classifiers for sleep stage classification

Cited by 35 publications

References 37 publications

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

Semi-Automated Sleep EEG Scoring with Active Learning and HMM-Based Deletion of Ambiguous Instances

Using hidden Markov models with raw, triaxial wrist accelerometry data to determine sleep stages

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