Detection of transients in heart rate variability signals using a time-varying varying Karhunen-Loeve expansion

We present a method to capture transient changes in heart-rate variability (HRV) signals. To deal with the nonstationarities in these signals we propose a time-dependent Karhunen-Loeve expansion. Using this expansion together with an entropy criterion we are able to partition the signal into quasi-stationary segments that can be further analyzed. Local eigenvalues and eigenvectors are then computed from a time-varying autocorrelation matrix in each segment. Application of our procedure resides in the dynamic assessment of the activity of the autonomic nervous system. An example is the capture of changes in HRV that might predict the initiation of cardiac arrhythmias. Previously, after performing and analyzing a stationary Karhunen-Loeve expansion of HRV signals, we showed that a significant increase in the dynamic range of the coefficients occurs shortly before the onset of life-threatening disturbances of cardiac rhythm. With the method proposed in this paper, we extend our previous results and we attempt a more precise determination of transients associated with changes in the autonomic nervous system.