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VMD-RiM: Rician Modeling of Temporal Feature Variation Extracted From Variational Mode Decomposed EEG Signal for Automatic Sleep Apnea Detection

Title:

VMD-RiM: Rician Modeling of Temporal Feature Variation Extracted From Variational Mode Decomposed EEG Signal for Automatic Sleep Apnea Detection

Bhattacharjee, Arnab, Fattah, Shaikh Anowarul, Zhu, Wei-Ping ORCID: https://orcid.org/0000-0001-7955-7044 and Ahmad, M. Omair ORCID: https://orcid.org/0000-0002-2924-6659 (2018) VMD-RiM: Rician Modeling of Temporal Feature Variation Extracted From Variational Mode Decomposed EEG Signal for Automatic Sleep Apnea Detection. IEEE Access, 6 . pp. 77440-77453. ISSN 2169-3536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2018.2883062

Abstract

Electroencephalogram (EEG) is getting special attention of late in the detection of sleep apnea as it is directly related to the neural activity. But apnea detection through visual monitoring of EEG signal by an expert is expensive, difficult, and susceptible to human error. To counter this problem, an automatic apnea detection scheme is proposed in this paper using a single lead EEG signal, which can differentiate apnea patients and healthy subjects and also classify apnea and non-apnea frames in the data of an apnea patient. Each sub-frame of a given frame of EEG data is first decomposed into band-limited intrinsic mode functions (BLIMFs) by using the variational mode decomposition (VMD). The advantage of using VMD is to obtain compact BLIMFs with adaptive center frequencies, which give an opportunity to capture the local information corresponding to varying neural activity. Furthermore, by extracting features from each BLIMF, a temporal within-frame feature variation pattern is obtained for each mode. We propose to fit the resulting pattern with the Rician model (RiM) and utilize the fitted model parameters as features. The use of such VMD-RiM features not only offers better feature quality but also ensures very low feature dimension. In order to evaluate the performance of the proposed method, K nearest neighbor classifier is used and various cross-validation schemes are carried out. Detailed experimentation is carried out on several apnea and healthy subjects of various apnea-hypopnea indices from three publicly available datasets and it is found that the proposed method achieves superior classification performances in comparison to those obtained by the existing methods, in terms of sensitivity, specificity, and accuracy.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Article
Refereed:Yes
Authors:Bhattacharjee, Arnab and Fattah, Shaikh Anowarul and Zhu, Wei-Ping and Ahmad, M. Omair
Journal or Publication:IEEE Access
Date:2018
Funders:
  • Concordia Open Access Author Fund
Digital Object Identifier (DOI):10.1109/ACCESS.2018.2883062
Keywords:EEG signal, entropy, goodness of feature, KNN classifier, model fitting, Rician model, sleep apnea, sub-framing, variational mode decomposition
ID Code:984891
Deposited By: KRISTA ALEXANDER
Deposited On:16 Jan 2019 18:30
Last Modified:16 Jan 2019 18:30
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