An Automatic Identification Method of Ventricular Parallel Rhythm

Abstract

The invention discloses a ventricular parasystole automatic recognition method. The method comprises the following steps: conducting waveform characteristic point localization on original multi-lead electrocardiogram data, offering an R wave position and morphological classification of QRS complex wave, judging sinus beats and ventricular beats, and generating RR inter-phase information having sinus beat and ventricular beat identifiers; calculating premature beat bigeminy inter-phase irregularity measurement in accordance with the RR inter-phase information having sinus beat and ventricular beat identifiers; classifying ventricular beat RR inter-phases within each sub-band; in accordance with classified intra-class information, offering sub-band ventricular ectopia inter-phase regularitymeasurement; and in accordance with the premature beat bigeminy inter-phase irregularity measurement index, ventricular ectopia inter-phase regularity measurement index as well as an occurrence rate in a ventricular ectopia inter-phase, judging whether ventricular parasystole occurs in each sub-band or not in accordance with a threshold value method. According to the method, ventricular parasystole recognition is conducted based on ventricular ectopia beat classification; and doctors can be effectively assisted in judging the ventricular parasystole.

Description

technical field [0001] The invention belongs to the technical field of data processing, and particularly relates to automatic identification of parallel heart rhythms in electrocardiogram ECG data, which is used to realize the identification of whether parallel heart rhythms occur in electrocardiogram data. Background technique [0002] Parallel arrhythmia refers to an active arrhythmia caused by the ectopic excitation foci and the dominant sinus node excitation pulse not recombining each other, and the two competing to control the heart beat. In particular, ventricular parallel rhythm is more common in structural heart disease, and the accompanying complications lead to serious and harmful consequences such as sudden cardiac death. Only 0.1%-0.15% of routine ECG examinations are diagnosed as ventricular parallel rhythm, and the detection rate of parallel rhythm is far less than the actual incidence. There are two reasons: the physiological mechanism of parallel rhythm forma...

AI Technical Summary

Problems solved by technology

The reasons why manual judgment is easy to miss the diagnosis are: artificially can only give a qualitative judgment on the irregularity of the joint rhythm of premature beats; since the observed ECG signal is a modulated parallel wave, the formed physiological mechanism determines the abnormality of ventricular ectopia. There are various forms of pulsation, which leads to manual judgment on the regularity of ventricular ectopic intervals, which is easy to miss and misjudgment

The existing research is based on the feature method of the electrocardiogram and uses the shape of the electrocardiogram to manually distinguish parallel heart rhythms, which requires manual identification of various shapes, high requirements for the number of heart beats, and high requirements for experience

To sum up, there is currently no effective automatic recognition method for parallel heart rhythms, and this case arose from this

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