Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 538314 10 pages doi 2011 538314 Research Article Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children L. M. Sepulveda-Cano 1 E. Gil 2 P. Laguna 2 and G. Castellanos-Dominguez1 1 Grupo de Procesamiento y Reconocimiento de Senaales Universidad Nacional de Colombia Km. 9 Via al Aeropuerto Campus La Nubia 17001000 Manizales Colombia 2 Communications Technology Group GTC Aragon Institute of Engineering Research I3A ISS University of Zaragoza CIBER-BBN Maria de Luna 1 50018 Zaragoza Spain Correspondence should be addressed to L. M. Sepulveda-Cano lmsepulvedac@ Received 1 July 2010 Revised 6 December 2010 Accepted 26 January 2011 Academic Editor Antonio Napolitano Copyright 2011 L. M. Sepulveda-Cano 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. This paper discusses the methodology for selecting a set of relevant nonstationary features to increase the specificity of the obstructive sleep apnea detector. Dynamic features are extracted from time-evolving spectral representation of photoplethysmography envelope recordings. In this regard a time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. For training aim this work analyzes the concrete set comprising filter banked dynamic features that include spectral centroids the cepstral coefficients as well as their timevariant energies. Performance of classifier accuracy is provided for the collected polysomnography recordings of 21 children. Moreover since the apnea diagnosing is based on analysis of set of fragments partitioned from the photoplethysmography envelope recordings a new approach for their indirect labeling .
