Cyclostationary Analysis for Heart Rate Variability
Michele Ambrosanio, Fabio Baselice*
Identifiers and Pagination:Year: 2018
First Page: 164
Last Page: 181
Publisher Id: TOBIOIJ-11-164
Article History:Received Date: 30/5/2018
Revision Received Date: 24/6/2018
Acceptance Date: 26/6/2018
Electronic publication date: 31/7/2018
Collection year: 2018
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
During the last years, cyclostationarity has emerged as a new approach for the analysis of a certain type of non-stationary signals. This theoretical tool allows us to identify periodicity in signals which cannot be identified easily but also to separate useful signals for other interfering contributions that overlap in the spectral support.
The aim of this work is the exploitation of cyclostationary theory to enhance standard methodologies for the study of heart rate variability. In this framework, a preliminary analysis on healthy patients is proposed to be extended further on pathological patients with the perspective to improve (hopefully) the diagnostic power of some cardiac dysfunctions due to the more complete set of information provided by this analysis.
The proposed approach involves an initial band-pass filtering step in the range 0.5 - 40 Hz of the recorded ECG signal, followed by a first-order derivative filter to reduce the effects of P and T waves and to emphasise the QRS contribution. After that, the auto-correlation function is evaluated and the Cyclic Power Spectrum (CPS) is computed. From this two-dimensional information, a one-dimensional plot is derived via the evaluation of a folded-projected CPS to be compared with standard Lomb-Scargle spectrum.
The proposed analysis has been tested on both numerical simulations as well as for the processing of real data which are available online in the Physionet database.
The proposed cyclostationary analysis has shown a good agreement with the results provided by the classical Lomb-Scargle spectrum in the processing of real data, underlining some contributions in the high-frequency bandwidth which are not visible by means of standard processing.