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Cited by 49EEG and Brain-Computer InterfacesSleep and related disordersEmotion and Mood Recognition

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Multi-modality of polysomnography signals’ fusion for automatic sleep scoring

Open access
Yan, Rui, Zhang, Chi, Spruyt, Karen, Wei, Lai, Wang, Zhiqiang, Tian, Lili, Li, Xueqiao, Ristaniemi, Tapani, Zhang, Jihui, Cong, Fengyu·2019·Biomedical Signal Processing and Control
DOI 10.1016/j.bspc.2018.10.001
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Abstract

The study aims to develop an automatic sleep scoring method by fusing different polysomnography (PSG) signals and further to investigate PSG signals’ contribution to the scoring result. Eight combinations of four modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) were considered to find the optimal fusion of PSG signals. A total of 232 features, covering statistical characters, frequency characters, time-frequency characters, fractal characters, entropy characters and nonlinear characters, were derived from these PSG signals. To select the optimal features for each signal fusion, four widely used feature selection methods were compared. At the classification stage, five different classifiers were employed to evaluate the validity of the features and to classify sleep stages. For the database in the present study, the best classifier, random forest, realized the optimal consistency of 86.24% with the sleep macrostructures scored by the technologists trained at the Sleep Center. The optimal accuracy was achieved by fusing four modalities of PSG signals. Specifically, the top twelve features in the optimal feature set were respectively EEG features named zero-crossings, spectral edge, relative power spectral of theta, Petrosian fractal dimension, approximate entropy, permutation entropy and spectral entropy, and EOG features named spectral edge, approximate entropy, permutation entropy and spectral entropy, and the mutual information between EEG and submental EMG. In addition, ECG features (e.g. Petrosian fractal dimension, zero-crossings, mean value of R amplitude and permutation entropy) were useful for the discrimination among W, S1 and R. Through exploring the different fusions of multi-modality signals, the present study concluded that the multi-modality of PSG signals’ fusion contributed to higher accuracy, and the optimal feature set was a fusion of multiple types of features. Besides, compared with manual scoring, the proposed automatic scoring methods were cost-effective, which would alleviate the burden of the physicians, speed up sleep scoring, and expedite sleep research.

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Crossref · 10.1016/j.bspc.2018.10.001

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  • ML and deep learning approaches have gained traction in recent years for classifying sleep-wake cycles and sleep stages in multi-modal sensor data.

    “Machine learning and deep learning approaches have gained traction in recent years for the task of classifying sleep–wake cycles and sleep stages in multi-modal sensor data108,109.”

    From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [108]

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Metadata

Authors
Yan, R., Zhang, C., Spruyt, K., Wei, L., Wang, Z., Tian, L., Li, X., Ristaniemi, T., Zhang, J., & Cong, F.
Affiliations
  • Dalian University of Technology (CN)
  • Lyon 1 Université (FR)
  • Affiliated Zhongshan Hospital of Dalian University (CN)
  • University of Jyväskylä (FI)
  • Chinese University of Hong Kong (HK)
Corresponding author
  • Zhang, Jihui
  • Cong, Fengyu
ORCID
Venue
Biomedical Signal Processing and Control
Publisher
Elsevier BV
Published
2019-03
Volume
49
Pages
14-23
ISSN
1746-8094 (ISSN-L)
DOI
10.1016/j.bspc.2018.10.001
OpenAlex
W2900542257
Cited by
49
References
3
Citations by year
2026: 3 · 2025: 8 · 2024: 7 · 2023: 10 · 2022: 6 · 2021: 6 · 2020: 7 · 2019: 2
FWCI
2.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Biomedical Signal Processing and Control (publishedVersion, cc-by-nc-nd)
  • Jyväskylä University Digital Archive (University of Jyväskylä) (submittedVersion, cc-by-nc-nd)
  • HAL (submittedVersion)
Funders
  • National Natural Science Foundation of China (81471742, 91748105)
  • Dalian University of Technology
  • China Scholarship Council (91748105, 201606060227)
  • Fundamental Research Funds for the Central Universities (91748105, DUT16JJ(G)03)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Keywords
automated sleep scoring, sleep stage classification, EEG features, electrocardiographic findings, feature selection, random forest, fractal dimension, permutation entropy, spectral entropy

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