Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings
AuthorKhandoker, AH; Palaniswami, M; Karmakar, CK
Source TitleIEEE Transactions on Information Technology in Biomedicine
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
AffiliationElectrical and Electronic Engineering
Document TypeJournal Article
CitationsKhandoker, A. H., Palaniswami, M. & Karmakar, C. K. (2009). Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 13 (1), pp.37-48. https://doi.org/10.1109/TITB.2008.2004495.
Access StatusThis item is currently not available from this repository
ARC Grant codeARC/LP0454378
Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS - ) and subjects with OSAS (OSAS +), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS +/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS + subjects and 15 out of 16 OSAS - subjects (accuracy = 92.85%; Cohen's kappa value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.
KeywordsArtificial Intelligence and Image Processing
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