Use of SSA and MCSSA in the Analysis of Cardiac RR Time Series

A new preprocessing procedure in the analysis of cardiac RR interval time series is described. It uses the singular spectrum analysis (SSA) and the Monte Carlo SSA (MCSSA) test. A novel feature of this preprocessing procedure is the ability to identify the noise component present in the series with a given probability and to separate the time series into a trend, signal, and noise. The MCSSA test involves testing whether the modes obtained from SSA can be generated by a noise process leading to separation of the noise modes from the signal. The procedure described here does not discard or modify any sample in the record but merely separates the time series into a trend, signal, and noise, allowing for further analysis of these components. The procedure is not limited to the length of the record and could be applied to nonstationary data. The basis functions used in SSA are data adaptive in that they are not chosen a priori but instead are dependent on the data set used, increasing flexibility to the analysis. The procedure is illustrated using the RR interval time series of a healthy, congestive heart failure, and atrial fibrillation subject.