Modelling of polysomnographic respiratory measurements for artefact detection and signal restoration

Rathnayake, S. I., Abeyratne, U. R., Hukins, C. and Duce, B. (2008) Modelling of polysomnographic respiratory measurements for artefact detection and signal restoration. Physiological Measurement, 29 9: 999-1021. doi:10.1088/0967-3334/29/9/001

Author Rathnayake, S. I.
Abeyratne, U. R.
Hukins, C.
Duce, B.
Title Modelling of polysomnographic respiratory measurements for artefact detection and signal restoration
Journal name Physiological Measurement   Check publisher's open access policy
ISSN 0967-3334
Publication date 2008-09-01
Year available 2008
Sub-type Article (original research)
DOI 10.1088/0967-3334/29/9/001
Open Access Status Not yet assessed
Volume 29
Issue 9
Start page 999
End page 1021
Total pages 23
Editor Neuman, M.H.
Place of publication United Kingdom
Publisher Institute of Physics Publishing
Language eng
Subject C1
671402 Medical instrumentation
730305 Diagnostic methods
090303 Biomedical Instrumentation
Abstract Polysomnography (PSG), which incorporates measures of sleep with measures of EEG arousal, air flow, respiratory movement and oxygenation, is universally regarded as the reference standard in diagnosing sleep-related respiratory diseases such as obstructive sleep apnoea syndrome. Over 15 channels of physiological signals are measured from a subject undergoing a typical overnight PSG session. The signals often suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artefact-corrupted signal segments are visually detected and removed from further consideration. This is a highly time-consuming process, and subjective judgement is required for the job. During typical sleep scoring sessions, the target is the detection of segments of diagnostic interest, and signal restoration is not utilized for distorted segments. In this paper, we propose a novel framework for artefact detection and signal restoration based on the redundancy among respiratory flow signals. We focus on the air flow (thermistor sensors) and nasal pressure signals which are clinically significant in detecting respiratory disturbances. The method treats the respiratory system and other organs that provide respiratory-related inputs/outputs to it (e.g., cardiovascular, brain) as a possibly nonlinear coupled-dynamical system, and uses the celebrated Takens embedding theorem as the theoretical basis for signal prediction. Nonlinear prediction across time (self-prediction) and signals (cross-prediction) provides us with a mechanism to detect artefacts as unexplained deviations. In addition to detection, the proposed method carries the potential to correct certain classes of artefacts and restore the signal. In this study, we categorize commonly occurring artefacts and distortions in air flow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. The results we obtained from a database of clinical PSG signals indicated that the proposed technique can detect artefacts/distortions with a sensitivity >88.3% and specificity >92.4%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods.
Keyword Respiratory measurements
Non-linear modelling
Artefact detection
Signal restoration
Sleep disordered breathing
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2009 Higher Education Research Data Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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Created: Sat, 11 Apr 2009, 02:05:58 EST by Ms Kimberley Nunes on behalf of School of Information Technol and Elec Engineering