Automated recognition of patients with obstructive sleep apnoea using wavelet-based features of electrocardiogram recordings
Ahsan H Khandoker, Chandan K Karmakar, Marimuthu Palaniswami
COMPUTERS IN BIOLOGY AND MEDICINE | PERGAMON-ELSEVIER SCIENCE LTD | Published : 2009
Patients with obstructive sleep apnoea syndrome (OSAS) are at increased risk of developing hypertension and other cardiovascular diseases. This paper explores the use of support vector machines (SVMs) for automated recognition of patients with OSAS types (+/-) using features extracted from nocturnal ECG recordings, and compares its performance with other classifiers. Features extracted from wavelet decomposition of heart rate variability (HRV) and ECG-derived respiration (EDR) signals of whole records (30 learning sets from physionet) are presented as inputs to train the SVM classifier to recognize OSAS+/- subjects. The optimal SVM parameter set is then determined by using a leave-one-out pr..View full abstract
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Awarded by Australian Research Council (ARC)
This study was supported by an Australian Research Council (ARC) linkage project with Compumedics Pty Ltd (LP0454378). The authors would like to thank Dr Slaven Marusic and Dr Mak Daulatzai of University of Melbourne for revising and editing the manuscript.