Hierarchical parallel PSO-SVM based subject-independent sleep apnea classification

Yashar Maali, Adel Al-Jumaily

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

7 Citations (Scopus)

Abstract

This paper presents a method for subject independent classification of sleep apnea by a parallel PSO-SVM algorithm. In the proposed structure, swarms are separated into masters and slaves and accessing to the global information is restricted according to their types. Biosignal records that used as the input of the system are air flow, thoracic and abdominal respiratory movement signals. The classification method consists of the three main parts; feature generation, feature selection and data reduction based on parallel PSO-SVM, and the final classification. Statistical analyses on the achieved results show efficiency of the proposed system.
Original languageEnglish
Title of host publicationNeural information processing
Subtitle of host publication19th international conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012 : proceedings
EditorsTingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung
Place of PublicationBerlin Heidelberg
PublisherSpringer, Springer Nature
Pages500-507
Number of pages8
Volume4
ISBN (Print)9783642344770
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventInternational Conference on Neural Information Processing (19th : 2012) - Doha, Qatar, Qatar
Duration: 12 Nov 201215 Nov 2012

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume7666
ISSN (Print)0302-9743

Conference

ConferenceInternational Conference on Neural Information Processing (19th : 2012)
CountryQatar
CityDoha, Qatar
Period12/11/1215/11/12

Keywords

  • sleep apnea
  • particle swarm optimisation
  • parallel processing
  • support vector machines

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