J wave classifying method based on support vector machine

Abstract

The invention relates to a J wave classifying method, in particular to a J wave classifying method based on a support vector machine. A blind source separating technology serves as a basis, the support vector machine serves as a main means, and automatic classification on benignant electrocardiosignal containing J waves and malignant electrocardiosignal containing J waves can be carried out. By the classification on the benignant electrocardiosignal containing the J waves and the malignant electrocardiosignal containing the J waves, high-risk patients whose electrocardiosignal contains abnormal J waves clinically can be recognized by doctors, the possibility of malignant premature bipolar syndrome, malignant arrhythmia and idiopathic ventricular fibrillation sudden death can be reduced, and a theory and practice basis is provided for accurate diagnosis on high-risk states of the J waves clinically. In the J wave classifying method based on the support vector machine, three subjects including medical science, blind source separation and machine learning are combined. The J wave classifying method based on the support vector machine belongs to frontier research hotspots in the domestic and foreign relevant field.

Description

technical field [0001] The invention relates to a J-wave classification method, in particular to a J-wave classification method based on a support vector machine. Background technique [0002] J wave, also known as Osborn wave, mainly manifests as an obvious baseline deviation phenomenon at the junction of QRS wave group and ST segment (J point) on the electrocardiogram, with a certain amplitude and time limit, and presents a special shape in the electrocardiogram waveform. J wave often appears in the process of the depolarization to repolarization transition of the ventricle. Due to its special location, it has been paid more and more attention in clinical practice. [0003] There are two ways to cause J-wave ECG variation, one is physiological ECG variation, the main manifestation is premature repolarization syndrome, and the other is pathological variation caused by hypothermia, hypercalcemia, traumatic brain injury, etc. , such as Brugada syndrome, sudden death syndrom...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a J-wave classification method based on support vector machines in order to solve the problem that the existing method for distinguishing J-wave benign and malignant is not only time-consuming but also fails to achieve ideal accuracy

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