Detection of Obstructive Sleep Apnea Through Heart Rate Variability
DOI:
https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1084Keywords:
Diagnosis, Heart Rate, Sleep Apnea SyndromesAbstract
Obstructive sleep apnea (OSA) is a respiratory problem that interferes with human quality of life. The detection of OSA can be done indirectly through the analysis of heart rate variability (HRV). In this context, this work investigates the use of HRV in the detection of OSA. For this, a set of ECG recordings from a database of individuals suffering from OSA was used in the study. First, statistical measurements of the HRV are extracted in the time and frequency domains of each 5-minute stretch of the ECG signal, which serve as input characteristics of the classifier. The following classifiers were implemented and compared: neural network (NN), k-nearest neighbors (KNN) and support vector machine (SVM). The results achieved in terms of accuracy were 79.3% for NN, 80.9% for KNN and 83.0% for SVM in detecting OSA.
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