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    Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings

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    235
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    Author
    Khandoker, AH; Palaniswami, M; Karmakar, CK
    Date
    2009-01-01
    Source Title
    IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
    Publisher
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
    University of Melbourne Author/s
    KHANDOKER, AHSAN; Palaniswami, Marimuthu; Karmakar, Chandan
    Affiliation
    Electrical and Electronic Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Khandoker, 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 Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/29269
    DOI
    10.1109/TITB.2008.2004495
    Abstract
    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.
    Keywords
    Artificial Intelligence and Image Processing

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