Pseudo differential heart beat and abnormal heart beat recognition method based on misclassification and supervised learning

Abstract

The invention relates to a pseudo differential heart beat and abnormal heart beat recognition algorithm based on misclassification and supervised learning. The algorithm comprises the following stepsthat 1, electrocardiogram data of an electrocardiogram signal database labeled with an existing heart beat type is adopted for recognizing R peaks and extracting heart beat features; 2, by comparing with the R peaks labeled in the database, the misrecognized R peaks and noise heart beats labeled in the database are classified to be pseudo differential heart beats; 3, heart beat features of eight types of the heart beats of the pseudo differential heart beats, normal heart beats, ventricular premature beats, ventricular escape beats, supraventricular premature beats, supraventricular escape beats, ventricular fusion beats and pacemaker heart beats in the database are extracted to serve as training data; 4, a supervised learning method is used for training the training data to be an eight classification model; 5, test data in real-time dynamic electrocardiogram data is extracted and input into the classification model to obtain the heart beat classification result. The algorithm is suitable for recognition of dynamic electrocardiogram long time electrocardiogram data pseudo differential heart beats and other multiple types of abnormal heart beats.

Description

technical field [0001] The invention relates to the technical field of automatic auxiliary detection of a dynamic electrocardiogram, in particular to a method for identifying false heart beats and other abnormal heart beats in a dynamic electrocardiogram. Background technique [0002] The ambulatory electrocardiogram is a long-term continuous recording of the body surface electrocardiogram, which contains more abundant physiological information of the human body than the conventional electrocardiogram, and can more objectively reflect and monitor the patient's physical condition. But at the same time, due to its long-term nature, the dynamic electrocardiogram contains a large number of heart beats and complicated types, which greatly increases the workload of doctors, which makes the automatic auxiliary detection technology of dynamic electrocardiogram increasingly important. [0003] Any waveform change in the ECG that is not caused by the electrical activity of the heart i...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the inadequacy of the prior art that cannot be applied to the identification of false heartbeats and other abnormal heartbeats in the long-term dynamic electrocardiogram data, the purpose of the present invention is to provide a false heartbeat and other abnormal heartbeats in the dynamic electrocardiogram The recognition method of this paper is based on misclassification firstly, classifying the heart beats detected by the marked ECG database as false heart beats, and then using the supervised learning method to classify the false heart beats and other abnormal heart beats, which is suitable for dynamic electrocardiogram Long-term electrocardiographic data false heartbeat and other abnormal heartbeat identification, effectively assisting doctors to quickly make related diagnoses

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