Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement

Jiang, Daowen; Lu, Yanan; Ma, Yu; Wang, Yuan‐Yuan

doi:10.1016/j.eswa.2018.12.023

Cited by 145 publications

(80 citation statements)

References 42 publications

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“…The number of epochs used in the experiments also require consideration. The performance achieved by our proposed method in the SC-task is comparable [15] in patient independent tasks. For a fair comparison between patient independent and not independent splitting, we implement the method proposed by Aboalayon et al [11], which obtained the best accuracy reported for this problem.…”

Section: Resultsmentioning

confidence: 76%

“…Our SC-task uses data from multiple patients (Fig 2) during test, which is more challenging compared to LOO-CV. This explains the accuracy difference between Jiang et al [15] and our proposed method (Table II). Fig.…”

Section: A Number Of Epochs and Splitmentioning

confidence: 72%

“…The other way is to perform patient splitting where patients are split between training and testing to ensure a patient independent test set [7], [10], [17]. Patient splitting can be performed at random [7] or in a leave-one-out cross-validation (LOO-CV) scheme where data from only one patient is used for evaluation [15]. The number of epochs used in the experiments also require consideration.…”

Section: Resultsmentioning

confidence: 99%

“…Digital filtering [11] and Wavelet Transform [12], [13], along with its variants [14], have been common pre-processing steps utilized in previous studies. A number of these methods extract time [11], frequency [14] or time-frequency features from each decomposed band of the signal [15]. These features are handengineered depending on the study population and therefore may not generalize well.…”

Section: Introductionmentioning

confidence: 99%

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End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets

Humayun

Sushmit

Hasan

et al. 2019

2019 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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Humans approximately spend a third of their life sleeping, which makes monitoring sleep an integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture for end-to-end sleep staging is proposed. The network takes raw single channel electroencephalogram (Fpz-Cz) signal as input and yields hypnogram annotations for each 30s segments as output. Experiments are carried out for two different scoring standards (5 and 6 stage classification) on the expanded PhysioNet Sleep-EDF dataset, which contains multi-source data from hospital and household polysomnography setups. The performance of the proposed network is compared with that of the state-of-the-art algorithms in patient independent validation tasks. The experimental results demonstrate the superiority of the proposed network compared to the best existing method, providing a relative improvement in epoch-wise average accuracy of 6.8% and 6.3% on the household data and multi-source data, respectively. Codes are made publicly available on Github.

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Section: Resultsmentioning

confidence: 76%

Section: A Number Of Epochs and Splitmentioning

confidence: 72%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets

Humayun

Sushmit

Hasan

et al. 2019

2019 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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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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Sleep Stages Detection Based BCI: A Novel Single-Channel EEG Classification Based on Optimized Bandpass Filter

Abenna

Nahid

Bouyghf

2022

Advanced Technologies for Humanity

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Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement

Cited by 145 publications

References 42 publications

End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets

End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets

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

Sleep Stages Detection Based BCI: A Novel Single-Channel EEG Classification Based on Optimized Bandpass Filter

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