Dynamic electrocardiogram T wave alternate quantitative analysis method based on models

Abstract

The invention discloses a dynamic electrocardiogram (ECG) T wave alternate quantitative analysis method based on models, which belongs to the technical field of biomedical signal processing. The dynamic electrocardiogram (ECG) T wave alternate quantitative analysis method includes steps of preprocessing 12 lead ambulatory electrocardiograms of a patient at first and removing random disturbance including baseline drift, power frequency disturbance, myoelectricity noise and the like; building various channel electrocardiosignal state space models and realizing robust estimation to electrocardiosignal waveforms by the aid of a dynamic multi-scale state estimation theory; applying a multi-sensor data fusion method to realize T wave fusion extraction and realizing T wave quantitative description; and finally realizing quantitative analysis for T wave alternate signals according to the analytic function of T waves. The dynamic electrocardiogram T wave alternate quantitative analysis method has the advantages that on the basis of the electrocardiosignal state spatial models, T wave quantitative analysis is realized at first, then dynamic electrocardiogram T wave alternate real-time detection and analysis are realized, and accordingly the dynamic electrocardiogram T wave alternate quantitative analysis method is convenient for catching T wave alternate electrocardio abnormal conditions suddenly caused in daily life and increases detecting level and diagnosis ability to patients in danger of sudden cardiac death.

Description

technical field [0001] The invention relates to the technical field of biomedical signal processing, in particular to a model-based method for quantitatively analyzing T-wave alternation in dynamic electrocardiograms, especially a method for quantitatively analyzing T-waves using multi-scale state estimation theory and multi-sensor data fusion theory Then realize the method of quantitative analysis of T wave alternation. Background technique [0002] T wave alternans (T Wave Alternans, TWA) refers to the alternation of T wave shape, amplitude and even polarity beat-by-beat in the body surface electrocardiogram (Electrocardiogram-ECG, including 5 parts, namely P, Q, R, S, and T waves). ECG variability. There is a very close relationship between microvolt-level T wave alternation and malignant arrhythmia and sudden cardiac death (SCD). Quantitative analysis of T wave alternation has very important clinical significance for cognition and prediction of sudden cardiac death. A...

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Problems solved by technology

[0007] T-wave extraction and alignment is the premise of T-wave alternate quantitative analysis. Dynamic ECG feature point positioning has been one of the technical problems in the field of automatic detection and analysis of ECG signals for many years. Unavoidable environmental noise and dynamic changes in PQRST waveform shape It makes it extremely difficult to accurately locate ECG feature points

In 2008, Sayadi proposed the model-based ECG feature point detection method for the first time, which has the advantage of high accuracy, but it can only be performed beat by beat, which is parameter estimation rather than state estimation

The advantage of the frequency domain method is high frequency resolution and insensitivity to adjacent frequency interference (baseline drift, etc.), but the disadvantage is that it does not have time resolution and cannot track non-stationary T wave alternation, so it is difficult to apply to the quantitative analysis of dynamic electrocardiogram TWA

The advantage of the time-domain method is that it has a good time resolution and can track the unsteady T-wave alternation phenomenon, but it has high requirements on the input quality of the signal and is very sensitive to adjacent frequency noise, which may easily cause false detection and missed detection.

Traditional time domain or frequency domain analysis methods are not enough to describe its non-stationary characteristics, and the analysis effect is not ideal

The time-frequency domain detection method mainly uses time-frequency analysis to locate feature points. After extracting T waves, it combines traditional time-domain and frequency-domain methods for TWA detection. It is not a real time-frequency detection. The detection method does not have Improve

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