Files and links (1)
Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features
Sensors (Basel, Switzerland), v 20(17), pp 1-21
01 Sep 2020
PMID: 32825024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen's and Kale's (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.
Metrics
Details
- Title
- An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features
- Creators
- Huaming Shen - Shanghai UniversityFeng Ran - Shanghai UniversityMeihua Xu - Shanghai UniversityAllon Guez - Drexel UniversityAng Li - Shanghai UniversityAiying Guo - Shanghai University
- Publication Details
- Sensors (Basel, Switzerland), v 20(17), pp 1-21
- Publisher
- Mdpi
- Number of pages
- 20
- Grant note
- 61674100 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Urban Health Collaborative
- Web of Science ID
- WOS:000571614800001
- Scopus ID
- 2-s2.0-85089558974
- Other Identifier
- 991019169372704721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Chemistry, Analytical
- Engineering, Electrical & Electronic
- Instruments & Instrumentation