Self-advising SVM for sleep apnea classification

Yashar Maali; Adel Al-Jumaily; Leon Laks

Self-advising SVM for sleep apnea classification

Yashar Maali, Adel Al-Jumaily, Leon Laks

Research output: Contribution to journal › Conference paper › peer-review

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 language	English
Pages (from-to)	24-33
Number of pages	10
Journal	Proceedings of the workshop on new trends of computational intelligence in health applications
Publication status	Published - 2012
Externally published	Yes
Event	Workshop on new trends of computational intelligence in health applications (CIHealth 2012) - Sydney, Australia Duration: 4 Dec 2012 → 4 Dec 2012

Keywords

Sleep apnea
Support vector machines
Particle swarm optimization

Cite this

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title = "Self-advising SVM for sleep apnea classification",

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.",

keywords = "Sleep apnea, Support vector machines, Particle swarm optimization",

author = "Yashar Maali and Adel Al-Jumaily and Leon Laks",

year = "2012",

language = "English",

pages = "24--33",

journal = "Proceedings of the workshop on new trends of computational intelligence in health applications",

issn = "1613-0073",

publisher = "Rheinisch-Westfaelische Technische Hochschule Aachen",

note = "Workshop on new trends of computational intelligence in health applications (CIHealth 2012) ; Conference date: 04-12-2012 Through 04-12-2012",

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TY - JOUR

T1 - Self-advising SVM for sleep apnea classification

AU - Maali, Yashar

AU - Al-Jumaily, Adel

AU - Laks, Leon

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Sleep apnea

KW - Support vector machines

KW - Particle swarm optimization

M3 - Conference paper

SN - 1613-0073

SP - 24

EP - 33

JO - Proceedings of the workshop on new trends of computational intelligence in health applications

JF - Proceedings of the workshop on new trends of computational intelligence in health applications

T2 - Workshop on new trends of computational intelligence in health applications (CIHealth 2012)

Y2 - 4 December 2012 through 4 December 2012

ER -

Self-advising SVM for sleep apnea classification

Abstract

Keywords

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