Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings
By Ahsan Habib Khandoker, Marimuthu Swami Palaniswami, Chandan Kumar Karmakar. 2009
351 citations recorded by the source corpus.
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.
Subfields: Cardiology and Cardiovascular Medicine, Experimental and Cognitive Psychology, Physiology
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In Cardiology and Cardiovascular Medicine
Citations in the corpus
- Federated learning of predictive models from federated Electronic Health Records (2018, background): “[6] Results in the literature suggest that sparse classifiers (i.e., those that rely on few features), have strong predictive power and generalize well out-of-sample,[7,8] providing at the same time interpretability in both models and results.”
- A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG (2015, background): “It has been shown by several authors [5]–[7], [16], [17] that respiratory events can be detected using an ECG derived respiration (EDR).”
- Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients (2018, neutral): “Among these methods, support vector machines (SVMs) and hidden Markov models (HMMs) are commonly used with the hand-crafted features that have produced acceptable results in [3]–[7].”