Segmenting cardiac-related data using sleep stages increases separation between normal subjects and apnoeic patients.

Inter-patient comparisons of cardiovascular metrics indicative of patient health have been shown to be successful in differentiating patients on a group rather than an individual level. This is in part due to the range of mental (as well as physical) activity-based variations for each patient and the difficulty assessing physical and mental activity during conscious states. In order to provide an objective scale for measuring central nervous system activity during sleep, the heart rate (RR) interval time series is divided into coarse sleep stage segments in which the LF/HF-ratio (the relative balance between low and high frequency power) is estimated for age and sex-matched populations of apnoeic and healthy subjects. Activity-based noise is therefore reduced and a more useful comparison of heart rate variability can be made. Additionally, the spectral estimation performances of the FFT and the Lomb-Scargle periodogram (LSP), a Fourier-based technique for unevenly sampled time series are compared. Separation of patients according to condition is shown to be more pronounced when using the LSP than the FFT. Furthermore, separation is found to be most marked in slow wave sleep.