Title :
Automatic detection of slow-wave-sleep using heart rate variability
Author :
Shinar, Z. ; Baharav, A. ; Dagan, Y. ; Akselrod, S.
Author_Institution :
Tel Aviv Univ., Israel
Abstract :
In this study, we used heart rate variability parameters to first characterize and then automatically detect slow-wave sleep (SWS). First, a wavelet transform was used to decompose equally sampled R-R interval series into their time-dependent spectral components: very low frequency (VLF) 0.005-0-04Hz, low frequency (LF) 0.04-0.15 Hz, and high frequency (HF) 0.15-0.45Hz. Then, the known decrease in LF power during SWS was confirmed and a linear relation between the average LF/HF balance throughout the night and the balance during SWS was found. Also, similar behaviour was found with the VLF power and the VLF/HF ratio. Finally, a decision algorithm with two criteria was defined using a training set of ECG recordings and applied to a test set. The results amounted to an 80% correct identification of SWS. The limitations of the study, as well as inherent differences between SWS definitions based on EEG and ECG, are discussed
Keywords :
electrocardiography; medical signal processing; sleep; spectral analysis; wavelet transforms; ECG recordings; EEG; LF/HF balance; VLF power; VLF/HF ratio; automatic slow-wave sleep detection; decision algorithm; equally-sampled R-R interval series decomposition; heart rate variability; linear relation; low-frequency power decrease; time-dependent spectral components; wavelet transform; Electrocardiography; Electroencephalography; Electromyography; Electrooculography; Frequency; Hafnium; Heart rate detection; Heart rate variability; Sleep; Testing;
Conference_Titel :
Computers in Cardiology 2001
Conference_Location :
Rotterdam
Print_ISBN :
0-7803-7266-2
DOI :
10.1109/CIC.2001.977725