| Summary: | Objective
It has previously been shown that there are morphological changes in hearth sounds during respiration and holding breath. In this study, for the first time in the literature, it was investigated whether sleep apnea could be detected automatically from heart sounds by teaching various classifiers of time and frequency plane parameters which are thought to be able to characterize the morphological changes seen in heart sounds during apnea.
Materials and Methods
For this purpose, heart sounds were recorded simultaneously with full polysomnography records from 17 people. Classification studies were performed by assigning feature vectors obtained from heart sounds to K nearest neighbors and support vector machines.
Results
The best result with K nearest neighbor classifier was 48% accuracy, 100% selectivity level. With support vector machines classifier, 82% accuracy and 42% selectivity values were reached.
Conclusion
According to these values, it is concluded that the parameters of the heart sound used in this study do not make it possible to diagnose the sleep apnea from the heart sounds.
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