Advanced daytime polysomnographic preprocessing: A versatile approach for stream-wise estimation

Chaparro-Vargas, R and Cvetkovic, D 2014, 'Advanced daytime polysomnographic preprocessing: A versatile approach for stream-wise estimation', Digital Signal Processing, vol. 35, pp. 95-104.


Document type: Journal Article
Collection: Journal Articles

Title Advanced daytime polysomnographic preprocessing: A versatile approach for stream-wise estimation
Author(s) Chaparro-Vargas, R
Cvetkovic, D
Year 2014
Journal name Digital Signal Processing
Volume number 35
Start page 95
End page 104
Total pages 10
Publisher Academic Press
Abstract The enhancement of monitoring biosignals plays a crucial role to thrive successfully computer-assisted diagnosis, ergo the deployment of outstanding approaches is an ongoing field of research demand. In the present article, a computational prototype for preprocessing short daytime polysomnographic (sdPSG) recordings based on advanced estimation techniques is introduced. The postulated model is capable of performing data segmentation, baseline correction, whitening, embedding artefacts removal and noise cancellation upon multivariate sdPSG data sets. The methodological framework includes Karhunen-Loève Transformation (KLT), Blind Source Separation with Second Order Statistics (BSS-SOS) and Wavelet Packet Transform (WPT) to attain low-order, time-to-diagnosis efficiency and modular autonomy. The data collected from 10 voluntary subjects were preprocessed by the model, in order to evaluate the withdrawal of noisy and artefactual activity from electroencephalographic (EEG) and electrooculographic (EOG) channels. The performance metrics are distinguished in qualitative (visual inspection) and quantitative manner, such as: Signal-to-Interference Ratio (SIR), Root Mean Square Error (RMSE) and Signal-to-Noise Ratio (SNR). The computational model demonstrated a complete artefact rejection in 80% of the preprocessed epochs, 4 to 8 dB for residual error and 12 to 30 dB in signal-to-noise gain after denoising trial. In comparison to previous approaches, N-way ANOVA tests were conducted to attest the prowess of the system in the improvement of electrophysiological signals to forthcoming processing and classification stages.
Subject Biomedical Instrumentation
Keyword(s) Artefacts rejection
EEG
Noise removal
Preprocessing
PSG sleep
DOI - identifier 10.1016/j.dsp.2014.09.007
Copyright notice © 2014 Elsevier Inc. All rights reserved.
ISSN 1051-2004
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