Electrical and Electronic Engineering - Research Publications

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Start date: 2009 × End date: 2009 × Author: Khandoker, Ahsan × Author: Karmakar, Chandan ×

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    Characterization Of Chimeric Surface Submentalis EMG Activity During Hypopneas In Obstructive Sleep Apnea Patients
    Daulatzai, MA ; Khandoker, AH ; Karmakar, CK ; Palaniswami, M ; Khan, N (IEEE, 2009)
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    Lateral Decubitus Posture during Sleep: Sub-Groups of Obstructive Sleep Apnea Patients - Therapeutic Value of Vertical Position in OSA
    Daulatzai, MA ; Khan, N ; Karmakar, C ; Khandoker, A ; Palaniswami, M ; Marusic, S ; Palaniswami, M ; Gubbi, J ; Law, YW (IEEE, 2009)
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    Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings
    Khandoker, AH ; Palaniswami, M ; Karmakar, CK (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2009-01)
    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.