Analysis method of ECG characteristics based on scatter diagram and symbolic dynamics

Abstract

The invention discloses an electrocardiographic feature analysis method based on scatter diagrams and symbol dynamics, which belongs to the field of electrocardiographic signal processing. After preprocessing the original ECG signal to be processed, perform R wave positioning and obtain the HRV sequence by calculating the interval between adjacent R waves; draw the ECG scatter diagram and use a set of 45°parallel lines to partition the ECG scatter diagram, Then, according to the order of the corresponding RR intervals of each scattered point, each scattered point is composed of a sequence with the area code of the partition where the scattered point is located, and then encoded and converted to obtain a decimal sequence, and the information entropy of the sequence is calculated to construct the feature vector, thus Classify and identify ECG signals, etc.

Description

technical field [0001] The invention proposes a heart rate variability analysis method, combined with a suitable classifier, which can effectively complete the identification and classification of different types of electrocardiographic signals, and belongs to the field of electrocardiographic signal processing. Background technique [0002] Heart rate variability (HRV) refers to the difference in the length of the interval between heartbeats in sinus rhythm within a certain period of time. HRV analysis is a class of methods to quantitatively assess autonomic and cardiac status. Through the analysis and processing of the HRV signal, the state of the heart, sympathetic nerve, vagus nerve, etc. and their mutual checks and balances can be obtained. Many methods have been applied to HRV research in recent years, but the traditional time domain and frequency domain analysis methods are widely used at present. Since the human body is a complex nonlinear system and the heart is e...

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