A Novel partially connected cooperative parallel PSO-SVM algorithm

study based on sleep apnea detection

Yashar Maali, Adel Al-Jumaily

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

15 Citations (Scopus)

Abstract

Sleep disorders are common in a general population. It effect one in 5 adults and has several short term and long term bad side effects on health. Sleep apnea (SA) is the most important and common component of sleep disorders. This paper presents an automatic approach for detecting apnea events by using few bio-singles that are related to breathe defect. This work uses only air flow, thoracic and abdominal respiratory movement as input. The proposed algorithm consists of three main parts which are signal segmentation, feature generation and classification. A new proposed segmentation method intelligently segments the input signals for further classification, then features are generated for each segment by wavelet packet coefficients and also original signals. In classification phase a unique parallel PSO-SVM algorithm is investigated. PSO used to tune SVM parameters, and also data reduction. Proposed parallel structure used to help PSO to search space more efficiently, also avoiding fast convergence and local optimal results that are common problem in similar parallel algorithms. Obtained results demonstrate that the proposed method is effective and robust in sleep apnea detection and statistical tests on the results shown superiority of it versus previous methods even with more input signals, and also versus single PSO-SVM. Using fewer signals means more comfortable to subject and also, reduction of cost during recording the data.
Original languageEnglish
Title of host publication2012 IEEE Congress on Evolutionary Computation
Place of PublicationUnited States
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)9781467315098
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventIEEE World Congress on Computational Intelligence (WCCI 2012) / IEEE Congress on Evolutionary Computation (IEEE-CEC 2012) - Brisbane, Australia
Duration: 10 Jun 201215 Jun 2012

Conference

ConferenceIEEE World Congress on Computational Intelligence (WCCI 2012) / IEEE Congress on Evolutionary Computation (IEEE-CEC 2012)
CityBrisbane, Australia
Period10/06/1215/06/12

Keywords

  • sleep apnea
  • PSO
  • parallel programming
  • SVM

Fingerprint Dive into the research topics of 'A Novel partially connected cooperative parallel PSO-SVM algorithm: study based on sleep apnea detection'. Together they form a unique fingerprint.

  • Cite this

    Maali, Y., & Al-Jumaily, A. (2012). A Novel partially connected cooperative parallel PSO-SVM algorithm: study based on sleep apnea detection. In 2012 IEEE Congress on Evolutionary Computation United States: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC.2012.6256138