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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 2750701, 10 pages
Research Article

Weighted Polynomial Approximation for Automated Detection of Inspiratory Flow Limitation

1Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan
2Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Keelung, Taiwan
3Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
4Technology Translation Center for Medical Device, Chung Yuan Christian University, Taoyuan, Taiwan
5Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan

Correspondence should be addressed to Wen-Chen Lin; wt.gro.ucyc@nehcnew_nil

Received 22 December 2016; Revised 13 March 2017; Accepted 23 April 2017; Published 28 May 2017

Academic Editor: Thomas Desaive

Copyright © 2017 Sheng-Cheng Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Inspiratory flow limitation (IFL) is a critical symptom of sleep breathing disorders. A characteristic flattened flow-time curve indicates the presence of highest resistance flow limitation. This study involved investigating a real-time algorithm for detecting IFL during sleep. Three categories of inspiratory flow shape were collected from previous studies for use as a development set. Of these, 16 cases were labeled as non-IFL and 78 as IFL which were further categorized into minor level (20 cases) and severe level (58 cases) of obstruction. In this study, algorithms using polynomial functions were proposed for extracting the features of IFL. Methods using first- to third-order polynomial approximations were applied to calculate the fitting curve to obtain the mean absolute error. The proposed algorithm is described by the weighted third-order (w.3rd-order) polynomial function. For validation, a total of 1,093 inspiratory breaths were acquired as a test set. The accuracy levels of the classifications produced by the presented feature detection methods were analyzed, and the performance levels were compared using a misclassification cobweb. According to the results, the algorithm using the w.3rd-order polynomial approximation achieved an accuracy of 94.14% for IFL classification. We concluded that this algorithm achieved effective automatic IFL detection during sleep.