Beat-to-beat cardiovascular hemodynamic parameters based on wavelet spectrogram of impedance data. (July 2017)

• Record Type: Journal Article
• Authors: Stepanov, Rodion; Podtaev, Sergey; Frick, Peter; Dumler, Andrey
• Is Part Of: Biomedical signal processing and control. Volume 36(2017)
• Page Start: 50
• Page End: 56
• Publication Date: 2017-07
• Subjects: Impedance cardiography -- Polyrheocardiography -- Wavelet analysis -- Data processing; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.03.012
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Highlights: Impedance cardiography is attractive low cost express-diagnostics. We use wavelet analysis to improve and infuse new life into impedance cardiography. Wavelets is useful for noise filtration and interpretation of the impedance cardiogram. Wavelet processing recognises artifacts, breath wave, and morphological peculiarities. Abstract: Impedance cardiography (ICG) is an attractive non-invasive tool for determining cardiac output, systemic vascular resistance and stroke volume (SV). New methods of improving the signal to noise ratio and data processing can infuse new life into this technique. In the present paper, wavelet transform is used to find solutions to two interrelated problems: (1) filtering of tetrapolar thoracic rheography signals (ICG, electrocardiogram and phonocardiogram (PCG)) to suppress random noise and respiratory variation; (2) quantitative description of the ICG and PCG parameters based on the analysis of two-dimensional time-frequency distributions of wavelet coefficients. The wavelet images provide an illustrative representation of the results obtained and allow us to define the ejection time and to control beat-to-beat changes in cardiac parameters in the systolic and diastolic phases of the cardiac cycle. The advantage of the wavelet-based approach is demonstrated by an example of determining the SV variation during the respiratory cycle. Wavelet-based processing of impedance cardiography signals reduces the influence of artifacts and the interference of breathing modulation, and displays the morphological features of cardiac cycles.