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

R A Thuraisingham

doi:10.1155/2013/231459

Abstract

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.

Highlights

Singular spectrum analysis (SSA) [1,2,3,4] is an analytical tool that is used in time series analysis
The above procedure is illustrated for three RR data sets obtained from N, congestive heart failure (CHF), and atrial fibrillation (AF) subject [13], These three RR series have different characteristics and will provide a good example to understand this technique
The method adopted here to construct the trend and the signal is an alternate procedure to the use of wavelet analysis and the use of impulse rejection filter [11]

Summary

Introduction

Singular spectrum analysis (SSA) [1,2,3,4] is an analytical tool that is used in time series analysis. The method proposed here uses SSA, followed by the Monte Carlo singular spectrum analysis test (MCSSA) [1, 12] It is a novel technique to separate the signal into various SSA modes and to identify the SSA modes which correspond to noise. The test identifies with a certain probability that certain SSA modes could be associated with autocorrelated noise The use of such a test helps in the separation of the raw time series into a signal and noise. The ability to identify certain SSA modes with noise processes of a given probability is a novel feature of the proposed procedure This provides a certain confidence in the identification of the signal. Further the basis functions used in SSA for the separation of the signal are not chosen a priori as in the wavelet procedure but instead are dependent on the data used

Methods

Results

Conclusion