Authors:
Rui Yan
1
;
Fan Li
2
;
Xiaoyu Wang
3
;
Tapani Ristaniemi
4
and
Fengyu Cong
1
Affiliations:
1
Faculty of Information Technology, University of Jyväskylä, 40014, Jyväskylä, Finland, School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian and China
;
2
School of Information Science and Engineering, Dalian Polytechnic University, 116034, Dalian and China
;
3
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian and China
;
4
Faculty of Information Technology, University of Jyväskylä, 40014, Jyväskylä and Finland
Keyword(s):
Polysomnography, Multi-modality Analysis, MATLAB Toolbox, Automatic Sleep Scoring.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Applications
;
Design and Implementation of Signal Processing Systems
;
Multimedia
;
Multimedia Signal Processing
;
Multimedia Systems and Applications
;
Multimodal Signal Processing
;
Telecommunications
Abstract:
Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. To speed up the process of sleep scoring without compromising accuracy, this paper develops an automatic sleep scoring toolbox with the capability of multi-signal processing. It allows the user to choose signal types and the number of target classes. Then, an automatic process containing signal pre-processing, feature extraction, classifier training (or prediction) and result correction will be performed. Finally, the application interface displays predicted sleep structure, related sleep parameters and the sleep quality index for reference. To improve the identification accuracy of minority stages, a layer-wise classification strategy is proposed according to the signal characteristics of sleep stages. The context of the current stage is taken into consideration in the correction phase by employing a Hidden Markov Model to study the transition rules of sleep stages in the training dataset. These trans
ition rules will be used for logic classification results. The performance of proposed toolbox has been tested on 100 subjects with an average accuracy of 85.76%. The proposed automatic scoring toolbox would alleviate the burden of the physicians, speed up sleep scoring, and expedite sleep research.
(More)