gms | German Medical Science

33rd International Congress on Electrocardiology

International Society of Electrocardiology

Analysis Of Heart Rate Dynamics In Congestive Heart Failure

Meeting Abstract

Search Medline for

  • corresponding author presenting/speaker V.D. Moga - University of Medicine and Pharmacy, Timisoara, Rumänien
  • M. Moga - Emergency Hospital No.1, Timisoara, Rumänien

33rd International Congress on Electrocardiology. Cologne, 28.06.-01.07.2006. Düsseldorf, Köln: German Medical Science; 2007. Doc06ice114

The electronic version of this article is the complete one and can be found online at:

Published: February 8, 2007

© 2007 Moga et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



Introduction: During the last years, wavelet transform has proven to be a valuable tool in many applications areas for analysis of non-stationary signals, and the ECG in particular. The result will be a collection of time-frequency representations of the signal, all with different resolutions.

Method: The study was performed at the Emergency Hospital No.1, University of Medicine and Pharmacy, Timisoara, Romania. In our study we compared the heart rate variability (HRV) and Continuous Wavelet Transform (CWT) time-frequency spectrum for ECG. For this purpose we used the Continuous Wavelet Transform, under MATLAB 6.5Ō and Autosignal v.1.6 from SYSTAT software, on ECG signals from the MIT-BIH Arrhythmia and ESC-ECG databases. In this study we analyzed 24 types of ECG signals: 8 in sinus rhythm and 16 from recordings of the heart failure ECG databases. The sampling frequency of this ECG signals was 250 Hz. In our study we used most the Mexican Hat and Paul wavelets.

Results: In this study we proved the value of the CWT for two purposes, first after wavelet decomposition of the ECG we isolated each ECG component of the original signal on the specific color spectrogram reflecting the frequency components of the ECG signal, secondary, comparing the CWT time – frequency spectra with heart rate variability parameters, the time – frequency spectra after CWT identifies clearly the components of the spectra.

Conclusions: Apparently two different methods, HRV and CWT, not very wide used in this form for ECG signal analysis could offer a lot of data regarding the autonomous imbalance in heart diseases. Identification of the ECG components after CWT could be a useful tool for biomedical signal analysis, more specifically to identify specific changes in the shape of the ECG components in various conditions.

Keywords: Continuous Wavelet Transform, ECG spectrogram, Power Spectral Density, Heart Rate Variability.