Automatic diagnosis method for electrocardiographic abnormality

Abstract

The invention discloses an automatic diagnosis method for electrocardiographic abnormality and relates to the technical field of automatic diagnosis of electrocardiographic abnormality. The learning capacity of an RNN neural network to a time sequence and the learning capability of CNN to spatial features are combined to learn features of the electrocardiographic biological signal, and abnormal electrocardiographs of different types are automatically characterized. Then, a deep neural network based classifier is constructed, which is trained using an electrocardiograph with type annotations to improve the accuracy of classification, and automatic classification of different arrhythmia types is achieved. According to the invention, the process of manually extracting features is avoided; then, time sequence features and spatial features of the electrocardiographs are learned using RNN and CNN to form the classifier, and the classifier is trained through supervised learning so that the classifier can automatically diagnose abnormal electrocardiographs. As a result, the accuracy of automatic diagnosis and classification of electrocardiographic abnormality is improved.

Description

technical field [0001] The invention relates to the technical field of automatic diagnosis of abnormal electrocardiogram, in particular to an automatic diagnosis method for abnormal electrocardiogram. Background technique [0002] Electrocardiography is the most direct method and basis for diagnosing many diseases related to the heart. It has been widely used in clinical practice for many years due to its economical, reliable, fast, and non-invasive measurement methods. The electrophysiological activity of the heart can be directly reflected on the electrocardiogram, and the waveform, cycle and other information contained in the electrocardiogram are powerful evidence for doctors to make a diagnosis. [0003] The automatic diagnosis of ECG abnormality is an important medical auxiliary function. It can automatically diagnose the human ECG signal directly through the computer, detect abnormal ECG bands, and classify the abnormal types at the same time. In addition to being re...

AI Technical Summary

Problems solved by technology

Each convolutional layer in the convolutional neural network is followed by a calculation layer for local averaging and secondary extraction. This unique feature extraction structure reduces the feature resolution.

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