Automatic sleep staging by simultaneous analysis of ECG and respiratory signals in long epochs
We evaluated the potential utility of combining ECG and respiratory signals in sleep staging. These signals are easily recorded and can be useful in home sleep monitoring systems. The SVM-RFE and SVM were used for the ranking and classification of extracted features, respectively. The extracted features from thoracic respiratory signals have higher ranking than ECG-derived respiratory features. The best result was obtained by using HRV and thoracic respiratory signals.
heart rate variability (hrv) ecg-derived respiration (edr) empirical mode decomposition (emd) discrete wavelet transform (dwt) detrended fluctuation analysis (dfa) support vector machine-recursive feature elimination (svm-rfe)