Automatic shockable rhythm identification and classification method combined with electrocardio time-frequency domain feature analysis

Abstract

The invention provides an automatic shockable rhythm identification and classification method combined with electrocardio time-frequency domain feature analysis. The method comprises specific steps as follows: S1, pretreating electrocardio signals; S2, automatically identifying cardiac arrest rhythms, and if discrimination conditions are not met, implementing S3; S3, on the basis of an integral coefficient band-pass filter, calculating the maximum amplitude proportion value (Pa), the average amplitude proportion value (Pb) and the average deviation proportion value (Pc) of output signals; S4, S5, S6 and S7, discriminating shockable rhythms and non-shockable rhythms according to frequency domain feature values such as the Pa, the Pb, the Pc and the like, and implementing S8 in case of failure; S8, calculating an electrocardio standard grid bar projection standard deviation; S9, discriminating the shockable rhythms and the non-shockable rhythms according to the standard deviation. The method can be applied to instruments and equipment which automatically identify and classify the shockable rhythms according to body surface electrocardiograms, the shockable rhythm identification sensitivity and the non-shockable rhythm specificity are improved, and the algorithm operating efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of medical electronics, in particular to a method for automatic identification and classification of electrocardiographic signals (ECG), and in particular to an electric shockable resettable device that can be used in an electrocardiographic monitor and an automatic external defibrillator (AED). Automatic identification and classification of rhythm heart rhythm methods and algorithms. Background technique [0002] According to the epidemiological research results of the US Center for Health Statistics, more than 50% of the total deaths of cardiovascular diseases are caused by sudden cardiac death (Sudden Cardiac Death, SCD). Clinical epidemiological studies have shown that in the past five decades, the incidence of SCD has increased, and 80% of SCD is caused by ventricular fibrillation (Ventric Fibrillation-VF, referred to as ventricular fibrillation) or sustained ventricular fibrillation. Tachycardia (Vent...

AI Technical Summary

Problems solved by technology

However, when the shape of the ECG signal changes greatly, the algorithm based on the ECG R wave recognition cannot adapt to the recognition of the shockable rhythm, which leads to the disadvantage of low sensitivity and specificity of the recognition

In 1994 and 1997, "IEEE Transactions on Biomedical Engineering" successively published "Multiway sequential hypothesis testing for tachyarrhythmia discrimination" and "Detecting ventricular tachycardia and fibrillation by complexity measure", reporting algorithms based on various transformations and complexity analysis , but this type of algorithm has the disadvantages of complex calculations and high requirements for hardware calculation loads, and cannot be used in portable devices such as AEDs.

Recently, "Computers in Cardiology" (2005) and "IEEE Transactions on Biomedical Engineering" (2007) successively reported "A new ventricular fibrillation detection algorithm for automated external defibrillators" and "Detecting Ventricular Fibrillation by Time-Delay Methods" This article is about the new algorithm of HILB. This kind of method and algorithm is based on the phase space reconstruction algorithm. Although the specificity has been greatly improved, the recognition sensitivity is low.

To sum up, although a variety of methods and algorithms for the recognition and classification of shockable cardiac rhythms have been publicly reported so far, there is still a lack of consideration of both the sensitivity and specificity of the recognition, and its computational complexity is far from sufficient. Exceeds the computational load of devices such as portable AEDs

Images