Self-Advising SVM for Sleep Apnea Classification

Publisher:
CEUR
Publication Type:
Conference Proceeding
Citation:
Workshop on New trends of computational intelligence in health applications, 2012, pp. 24 - 33
Issue Date:
2012-01
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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 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
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