A least-squares support vector machines classifier using four ECG-derived features achieved >85% accuracy for subject-independent and >90% for patient-specific sleep apnea classification.
Does an LS-SVM classifier using four single-lead ECG-derived features accurately identify sleep apnea events?
A computationally simple, four-feature LS-SVM classifier using single-lead ECG can accurately identify sleep apnea events, making it potentially suitable for home monitoring systems.
In this paper a methodology to identify sleep apnea events is presented. It uses four easily computable features, three generally known ones and a newly proposed feature. Of the three well known parameters, two are computed from the RR interval time series and the other one from the approximate respiratory signal derived from the ECG using principal component analysis (PCA). The fourth feature is proposed in this paper and it is computed from the principal components of the QRS complexes. Together with a least squares support vector machines (LS-SVM) classifier using an RBF kernel, these four features achieve an accuracy on test data larger than 85% for a subject independent classification, and of more than 90% for a patient specific approach. These values are comparable with other results in the literature, but have the advantage that their computation is straightforward and much simpler. This can be important when implemented in a home monitoring system, which typically has limited computational resources.
Varon et al. (Mon,) conducted a other in Sleep apnea. Least-squares support vector machines (LS-SVM) classifier on single lead ECG was evaluated on Classification accuracy. A least-squares support vector machines classifier using four ECG-derived features achieved >85% accuracy for subject-independent and >90% for patient-specific sleep apnea classification.