Automated detecting sleep apnea syndrome

a novel system based on genetic SVM

Yashar Maali, Adel Al-Jumaily

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

12 Citations (Scopus)

Abstract

Sleep Apnea (SA) is one of the common symptoms and important part of sleep disorders. It has consequences that affect all daily life activities and present danger to the patient and/or others. The common diagnose procedure is based on an overnight sleep test. The test is usually including recording of several bio-signals that used to detect this syndrome. The conventional approach of detecting the sleep apnea uses a manual analysis of most of bio-signals to achieve reasonable accuracy. The manual process is highly cost and time-consuming. This paper presents a novel automatic system for detecting Apnea events by using just few of bio-signals that are related to breathe defect. This work use only Air flow, thoracic and abdominal respiratory movement as inputs for the system. The proposed technique consists of three main parts which are signal segmentation, feature generation and classification based on genetic SVM. Results show efficiency of this system and its superiority versus previous methods with more bio-signals as input.
Original languageEnglish
Title of host publicationProceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS)
EditorsAjith Abraham, Mohamed Kamel, Ronald Yager, Albert Zomaya, Azah Kamilah Muda, Tzung-Pei Hong, Choo Yun Huoy
Place of PublicationUnited States
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages590-594
Number of pages5
ISBN (Print)9781457721502
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventInternational Conference on Hybrid Intelligent Systems (11th : 2011) - Malacca, Malaysia
Duration: 5 Dec 20118 Dec 2011

Conference

ConferenceInternational Conference on Hybrid Intelligent Systems (11th : 2011)
CityMalacca, Malaysia
Period5/12/118/12/11

Keywords

  • genetic algorithm
  • sleep apnea
  • support vector machine

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