Self-Advising SVM for Sleep Apnea Classification

New Trends of Computational Intelligence in Health Applications

计算机科学, 生命科学

Yashar Maali1 ; Adel Al-Jumaily1 ; Leon Laks2

Others  :  http://ceur-ws.org/Vol-944/cihealth3.pdf
PID  :  26937

学科分类：计算机科学（综合）

来源: CEUR

【 摘 要 】

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 thoracicmovement, as inputs. Statistical tests show that self-advising SVM performs better than traditional SVM in sleep apnea classification.