Multi-modality of polysomnography signals' fusion for automatic sleep scoring. (March 2019)

• Record Type: Journal Article
• Authors: Yan, Rui; Zhang, Chi; Spruyt, Karen; Wei, Lai; Wang, Zhiqiang; Tian, Lili; Li, Xueqiao; Ristaniemi, Tapani; Zhang, Jihui; Cong, Fengyu
• Is Part Of: Biomedical signal processing and control. Volume 49(2019)
• Page Start: 14
• Page End: 23
• Publication Date: 2019-03
• Subjects: Polysomnography -- Multi-modality analysis -- Rules of R&K -- Automatic sleep scoring; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.10.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An automatic sleep scoring method was proposed by fusing multi-modality of polysomnography signals. A discriminative analysis was performed to elucidate multi-modality signals' contributions to automatic sleep scoring. Different signals' fusions were compared to demonstrate the validity of multiple signals' fusion. Four feature selection methods and five classifiers were compared to provide a reference for further study. Efficacy of the proposed method was confirmed by an open source database. Abstract: Objective: 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. Methods: 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. Results: 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. Conclusions: 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.