It is shown that in the case of human heart rate, the scaling behavior of the correlation sum (calculated by the Grassberger-Procaccia algorithm) is a result of the interplay of various factors: finite resolution of the apparatus (finite-size effects), a wide dynamic range of mean heart rate, the amplitude of short-time variability being a decreasing function of the mean heart rate. This is done via constructing a simple model of heart rhythm: a signal with functionally modulated Gaussian noise. This model reproduces the scaling behavior of the correlation sum of real medical data. The value of the scaling exponent depends on all the above-mentioned factors, and is a certain measure of short-time variability of the signal.
-In order to reveal the possible correlation between the level of myocardial electrical instability assessed at Holter monitoring and certain ECG parameters characterizing ventricular repolarization 24-hours ECG recordings were analyzed in 91 patients with different grades of ventricular arrhythmias. The following parameters were calculated: RT-interval (RT) duration and variability, RT apex interval (RTa) duration and variability, areas of the first and second half of T-wave (S1, S2) and maximal rise and fall slopes of T-wave (k1, k2). An original signal processing algorithm for ECG was developed for that purpose. The results of the study suggest that complex analysis of certain T-wave parameters, as well as RT interval variability can be a useful tool for identification of patients at increased risk of sudden death.
Human heart rate fluctuates in a complex and non-stationary manner. Elaborating efficient and adequate tools for the analysis of such signals has been a great challenge for the researchers during last decades. Here, an overview of the main research results in this field is given. The following question are addressed: (a) what are the intrinsic features of the heart rate variability signal; (b) what are the most promising non-linear measures, bearing in mind clinical diagnostic and prognostic applications.
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