Self-advising SVM for sleep apnea classification

Yashar Maali, Adel Al-Jumaily, Leon Laks

Research output: Contribution to journalConference paper

Abstract

In this paper Self-Advising SVM, a new proposed version of SVM, is investigated for sleep apnea classification. Self-Advising SVM tries to transfer more information from training phase to the test phase in compare to the traditional SVM. In this paper Sleep apnea events are classified to central, obstructive or mixed, by using just three signals, airflow, abdominal and thoracic movement, as inputs. Statistical tests show that self-advising SVM performs better than traditional SVM in sleep apnea classification.
Original languageEnglish
Pages (from-to)24-33
Number of pages10
JournalProceedings of the workshop on new trends of computational intelligence in health applications
Publication statusPublished - 2012
Externally publishedYes
EventWorkshop on new trends of computational intelligence in health applications (CIHealth 2012) - Sydney, Australia
Duration: 4 Dec 20124 Dec 2012

Keywords

  • Sleep apnea
  • Support vector machines
  • Particle swarm optimization

Fingerprint Dive into the research topics of 'Self-advising SVM for sleep apnea classification'. Together they form a unique fingerprint.

Cite this