Processing Methods and ECG Signal Recognition Model

Abstract

In the work for processing the ECG signal, methods for determining the length of RR interval of ECG signal and calculating on its basis the boundaries of RR interval of ECG signal, geometric converting of RR intervals of ECG signal have been proposed. The proposed definition of the length of RR interval of ECG signal uses statistical estimation of local maximum and band-pass filtering, which decreases the computational complexity, and decreases the dependence on noise and permit to use dynamic threshold, which increases the accuracy of calculating the length and boundaries of RR intervals of ECG signal. The proposed geometric converting of RR intervals of ECG signal makes it possible to convert RR intervals to a unified amplitude-time window, which permits to form samples of ECG signal on basis its structure. The proposed model of ECG signal recognition is based on adaptive probabilistic neural network that allows identification of the structure and parameters, which increases the recognition probability. The proposed method for identifying the structure and parameters of the model for recognizing ECG signal samples is based on adaptive clustering, which provides a high degree of compression and clustering of ECG signal samples. To evaluate the proposed methods and model, quality criteria are determined. Numerical studies, which allow to evaluate the proposed methods and model, have been carried out. The proposed methods and model make it possible to formulate and solve the problems of structuring, transforming and recognizing the ECG signal, which is used for ECG diagnostics.