Abstract
The proper evaluation of heart health requires professional medical experience. Therefore, in clinical diagnosis practice, the development direction is to reduce the high dependence of the diagnosis process on medical experience and to more effectively improve the diagnosis efficiency and accuracy. Deep learning has made remarkable achievements in intelligent image analysis technology involved in the medical process. From the aspect of cardiac diagnosis, image analysis can extract more profound and abundant information than sequential electrocardiogram (ECG) signals. Therefore, a new region recognition and diagnosis method model of a two-dimensional ECG (2D-ECG) signal based on an image format is proposed. This method can identify and diagnose each refined waveform in the cardiac conduction cycle reflected in the image format ECG signal, so as to realize the rapid and accurate positioning and visualization of the target recognition area and finally get the analysis results of specific diseases. The test results show that compared with the results obtained by a one-dimensional sequential ECG signal, the proposed model has higher average diagnostic accuracy (98.94%) and can assist doctors in disease diagnosis with better visualization effect.
Highlights
Due to the widespread attention of computer technology, computer-aided diagnosis technology has made rapid development in medical related fields
In view of the existing problems, combined with the theory of deep learning technology, this paper proposes a 2D-ECG faster region recognition and diagnosis method model
The 2D-ECG image data are trained based on Faster R-CNN to determine the diagnostic model of heart disease
Summary
Due to the widespread attention of computer technology, computer-aided diagnosis technology has made rapid development in medical related fields. It plays a positive role in intelligent diagnosis. Its research directions include brain waves, electromyography signals, cancer, the nervous system [1,2,3,4], heart and other clinical applications, and even more extensive directions of refinement are involved in clinical practice [5,6,7]. ECG signals are mainly based on the cardiac electrophysiological signal, which can effectively reflect various physiological states of human heart. It is usually acquired by the lead channel through body surface electrodes. Taking ECG signals as the research object in one-dimensional mode, Pyakillya B et al [14] used the convolution layer as a feature extractor and the fully convolutional layer as a final decision-making structure to classify
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