Classification Methods from Heart Rate Variability to Assist in SAHS Diagnosis

The aim of this study is to analyze different feature classification methods applied to heart rate variability (HRV) signals in order to help in sleep apnea-hypopnea syndrome (SAHS) diagnosis. A total of 240 recordings from patients suspected of suffering from SAHS were available. This initial dataset was divided into training set (96 subjects) and test set (144 subjects). For this study, spectral and nonlinear features have been extracted. Spectral characteristics were obtained from the power spectral density (PSD) from HRV records. On the other hand, the nonlinear features were obtained from HRV records in the time domain. Afterwards, some features were selected automatically by forward stepwise logistic regression (FSLR). We constructed two classifiers based on logistic regression (LR) and support vector machines (SVMs) with the selected features. Our results suggest that there are significant differences in various spectral and nonlinear parameters between SAHS positive and SAHS negative groups. The highest sensitivity, specificity and accuracy values were reached by the SVMs classifier: 70.8%, 79.2% and 73.6%, respectively. Results showed that feature selection of optimumcharacteristics from HRV signals could be useful to assist in SAHS diagnosis.