Self-advising SVM for sleep apnea classification

Publication Type:
Conference Proceeding
CEUR Workshop Proceedings, 2012, 944 pp. 24 - 33
Issue Date:
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In this paper Self-Advising SVM, a new proposed version of SVM, is investigated for sleep apnea classification. Self-Advising SVM tries to trans-fer 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, ob-structive 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.
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