In this paper, we are interested in the development of a compression method using wavelets for the Electrocardiogram (ECG) signal. The choice of the analyzing wavelet is of great importance for an optimal compression. Results obtained by the proposed algorithm consist of weak Compression Ratios (CR) for an acceptable Percentage RMS Difference (PRD) of the signal. Tests and measurements achieved by the application of our compression algorithm on the entire arrhythmias database MIT-BIH allow us to select, for a better compression, the adequate wavelets having from three to seven zero moments.
In this study, we have compared the efficiency of the short time Fourier transform (STFT) and autoregressive modelling (AR) and autoregressive moving average (ARMA) of the femoral Doppler artery ultrasonic signals, in order to determine the spectral broadening index (SBI). Our aim is to detect the impact of the two modelling approaches on sonograms and of power spectral density- frequency diagrams obtained from femoral arterial Doppler Signals. The sonograms have been then used to compare the methods in terms of their frequency resolution and effects in determining the stenosis of femoral artery. In this paper we have used generated frequency envelopes from the Doppler spectrum to determine an index showing the degree of severity of stenosis cases. This index called broadening spectral index is calculated for various real cases.
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