Automated breath detection on long-duration signals using feedforward backpropagation artificial neural networks.

Full Abstract

A new breath-detection algorithm is presented, intended to automate the analysis of respiratory data acquired during sleep. The algorithm is based on two independent artificial neural networks (ANN(insp) and ANN(expi)) that recognize, in the original signal, windows of interest where the onset of inspiration and expiration occurs. Postprocessing consists in finding inside each of these windows of interest minimum and maximum corresponding to each inspiration and expiration. The ANN(insp) and ANN(expi) correctly determine respectively 98.0% and 98.7% of the desired windows, when compared with 29,820 inspirations and 29,819 expirations detected by a human expert, obtained from three entire-night recordings. Postprocessing allowed determination of inspiration and expiration onsets with a mean difference with respect to the same human expert of (mean +/- SD) 34 +/- 71 ms for inspiration and 5 +/- 46 ms for expiration. The method proved to be effective in detecting the onset of inspiration and expiration in full night continuous recordings. A comparison of five human experts performing the same classification task yielded that the automated algorithm was undifferentiable from these human experts, falling within the distribution of human expert results. Besides being applicable to adult respiratory volume data, the presented algorithm was also successfully applied to infant sleep data, consisting of uncalibrated rib cage and abdominal movement recordings. A comparison with two previously published algorithms for breath detection in respiratory volume signal shows that the presented algorithm has a higher specificity, while presenting similar or higher positive predictive values.

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Author information

Author/s: Sá, Rui Carlos (RC); Verbandt, Yves (Y);

Affiliation: Laboratoire de Physique Biomédicale, Université Libre de Bruxelles, Brussels, Belgium. ruicarsa@ulb.ac.be

Journal and publication information

Publication Type: Comparative Study; Journal Article; Research Support, Non-U.S. Gov't; Validation Studies

Journal: IEEE transactions on bio-medical engineering (IEEE Trans Biomed Eng), published in United States. (Language: eng)

Reference: 2002-Oct; vol 49 (issue 10) : pp 1130-41

Dates: Created 2002/10/10; Completed 2002/11/13; Revised 2006/11/15;

PMID: 12374337, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Adult
Algorithms
Breath Tests - methods
Computer Simulation
Databases, Factual
Feedback
Humans
Infant
Male
Movement - physiology
Neural Networks (Computer)

Observer Variation
ROC Curve
Reproducibility of Results
Respiratory Function Tests - methods
Respiratory Mechanics - physiology
Respiratory Physiology
Sensitivity and Specificity
Sleep - physiology
Space Flight
Time Factors
Weightlessness

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