Abstract
This paper combines echocardiographic signal processing and artificial intelligence technology to propose a deep neural network model adapted to echocardiographic signals to achieve left atrial volume measurement and automatic assessment of pulmonary veins efficiently and quickly. Based on the echocardiographic signal generation mechanism and detection method, an experimental scheme for the echocardiographic signal acquisition was designed. The echocardiographic signal data of healthy subjects were measured in four different experimental states, and a database of left atrial volume measurements and pulmonary veins was constructed. Combining the correspondence between ECG signals and echocardiographic signals in the time domain, a series of preprocessing such as denoising, feature point localization, and segmentation of the cardiac cycle was realized by wavelet transform and threshold method to complete the data collection. This paper proposes a comparative model based on artificial intelligence, adapts to the characteristics of one-dimensional time-series echocardiographic signals, automatically extracts the deep features of echocardiographic signals, effectively reduces the subjective influence of manual feature selection, and realizes the automatic classification and evaluation of human left atrial volume measurement and pulmonary veins under different states. The experimental results show that the proposed BP neural network model has good adaptability and classification performance in the tasks of LV volume measurement and pulmonary vein automatic classification evaluation and achieves an average test accuracy of over 96.58%. The average root-mean-square error percentage of signal compression is only 0.65% by extracting the coding features of the original echocardiographic signal through the convolutional autoencoder, which completes the signal compression with low loss. Comparing the training time and classification accuracy of the LSTM network with the original signal and encoded features, the experimental results show that the AI model can greatly reduce the model training time cost and achieve an average accuracy of 97.97% in the test set and increase the real-time performance of the left atrial volume measurement and pulmonary vein evaluation as well as the security of the data transmission process, which is very important for the comparison of left atrial volume measurement and pulmonary vein. It is of great practical importance to compare left atrial volume measurements with pulmonary veins.
Highlights
In recent years, the procedure has been gradually refined and matured and has demonstrated more outstanding safety and efficacy than traditional drug therapy, leaping to become the first-line treatment option for patients with atrial fibrillation [1]. e increase in the surgical base has led to a relative increase in complications, which include pulmonary vein stenosis (PVS), pericardial effusion, arteriovenous embolism, and atrioventricular oesophageal fistula. e cardiac impedance signal is an impedance change signal measured directly from the body surface of the human chest using bioimpedance technology
Since the cardiac impedance signal has no negative waveform, it is often differentiated to reflect the pumping function of the heart from a hemodynamic perspective [2]. e pumping function of the heart can effectively reflect the location of the lesion in patients with cardiovascular diseases, the physical condition of the body, and the level of exercise training and is one of the important reference bases for the diagnosis of various Journal of Healthcare Engineering cardiovascular diseases [3, 4]. erefore, left atrial volume measurement and pulmonary vein assessment are of great importance for guiding treatment and assessing the functional status of the heart in patients with cardiovascular diseases
Neural networks are designed to analyze and process the given data by stimulating the activity of the human brain, and they have the same ability to learn by experience as that of the human brain. e echocardiographic left atrial volume measurement and pulmonary vein comparison model based on the neural network learning algorithm used in this study is suitable for dealing with the most complex nonlinear correlation problems seen in real practice, unlike the various risk comparison methods currently available, and provides a way to overcome the limitations of traditional statistical methods
Summary
The procedure has been gradually refined and matured and has demonstrated more outstanding safety and efficacy than traditional drug therapy, leaping to become the first-line treatment option for patients with atrial fibrillation [1]. e increase in the surgical base has led to a relative increase in complications, which include pulmonary vein stenosis (PVS), pericardial effusion, arteriovenous embolism, and atrioventricular oesophageal fistula. e cardiac impedance signal is an impedance change signal measured directly from the body surface of the human chest using bioimpedance technology. Erefore, left atrial volume measurement and pulmonary vein assessment are of great importance for guiding treatment and assessing the functional status of the heart in patients with cardiovascular diseases. E purpose of this paper is to establish a comparative model based on the echocardiographic findings of the patients, by applying the BP neural network learning algorithm jointly by applying the 7 indicators reflecting the cardiac function of the patients obtained by echocardiography, and to conduct a comparative study on the comparative results of patients with reduced ejection fraction, that is, 1-year readmission and 3-year mortality, which has high clinical research and practical application significance. E echocardiographic technique, as a clinical medical test for a comprehensive understanding of cardiac structure and function, has not been effectively used in left atrial volume measurement and pulmonary vein comparison. The validity of the model is verified by comparing the original data with the signal encoding features. e fifth chapter summarizes the research content of this paper
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