Table of Contents
Journal of Computational Medicine
Volume 2013, Article ID 231459, 7 pages
http://dx.doi.org/10.1155/2013/231459
Research Article

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

1A, Russell Street, Eastwood, NSW 2122, Australia

Received 18 October 2013; Revised 23 November 2013; Accepted 23 November 2013

Academic Editor: Gabriela Mustata Wilson

Copyright © 2013 R. A. Thuraisingham. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.