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This paper is part of the electrophysiological signal processing framework. The impedance cardiography signal (ICG) is considered as one of the most interesting electrophysiological signals. This signal provides information about the physio-pathological statue of the cardiovascular system of the patients. A suitable preprocessing is always required before any type of treatment applied to this signal. Our study is interested, in a first step, on the denoising of ICG signals: a comparison is carried out between several denoising techniques in order to specify the most effective method without distorting the shape of the studied signal. In a second step, this paper presents our detection algorithm of the characteristic points B, C and X of the denoised ICG signal. An evaluation test of this technique is performed and allowed to choose the Savitzky-Golay filter which gave an excellent positive predictivity P+ varying from 93.1% to 100% according to the characteristic points and a low detection error rate DE varying from 0 to 6.9%.

The innovation of our method is to design a new technique with multiple capabilities, autonomy and automatic applications in the diagnosis of some cardiovascular parameters relative to a certain categories of patients (elderly, children, pregnant women) with cardiovascular diseases. The objective of this work is to perform automatic diagnosis by processing the bioimpedance signal or impedance cardiography signal (ICG) which represents the aorta impedance variation during the heart cycle activity. Our analysis is based on the determination of ICG cepstral parameters (seven cepstral parameters). The application of the discriminant analysis method allowed us to perform the diagnosis of 168 anonymous cases. The seven cepstral parameters are sufficient to perform the automatic diagnosis of the cardiovascular system abnormalities with 97.2% of correctly classified cases.