ObjectiveTo explore the anesthesia management experience in the interventional treatment of pediatric congenital heart diseases (CHD) percutaneously guided by transthoracic echocardiography (TTE) on a mobile operating platform. Methods From March to July 2023, a total of 13 patients from remote areas underwent interventional treatment for CHD on the mobile operating platform of Fuwai Yunnan Cardiovascular Hospital. Patients who received non-tracheal intubation general anesthesia were retrospectively included. ResultsEight children who had difficulty cooperating with the surgery (due to young age, emotional tension, crying) received monitored anesthesia care with local anesthesia supplemented by sedative and analgesic drugs while maintaining spontaneous breathing under the monitoring and management of an anesthesiologist (i.e., non-tracheal intubation general anesthesia). Among them, there were 5 males and 3 females, with an age of (6.95±3.29) years and a body weight of (19.50±6.04) kg. Through transthoracic echocardiography, they were diagnosed with atrial septal defect (6 patients), residual shunt after patent ductus arteriosus ligation (1 patient), and severe pulmonary valve stenosis (1 patient). The surgery proceeded smoothly, with satisfactory anesthesia and surgical effects, complete analgesia, and satisfactory postoperative recovery. There was 1 patient of body movement and 1 patient of respiratory depression during the operation, and both patients completed the surgery successfully after treatment. All children had no serious surgery- and anesthesia-related complications. The anesthesia time was 40.5 (34.5, 47.5) min, the surgery time was 39.0 (33.0, 45.5) min, and the recovery time was 43.0 (28.0, 52.5) min Conclusion Interventional surgery for CHD guided by TTE at a mobile platform is a minimally invasive approach without radiation damage. Non-tracheal intubation general anesthesia with spontaneous breathing can be safely and effectively implemented in children who cannot cooperate.
Cardiac auscultation is the basic way for primary diagnosis and screening of congenital heart disease(CHD). A new classification algorithm of CHD based on convolution neural network was proposed for analysis and classification of CHD heart sounds in this work. The algorithm was based on the clinically collected diagnosed CHD heart sound signal. Firstly the heart sound signal preprocessing algorithm was used to extract and organize the Mel Cepstral Coefficient (MFSC) of the heart sound signal in the one-dimensional time domain and turn it into a two-dimensional feature sample. Secondly, 1 000 feature samples were used to train and optimize the convolutional neural network, and the training results with the accuracy of 0.896 and the loss value of 0.25 were obtained by using the Adam optimizer. Finally, 200 samples were tested with convolution neural network, and the results showed that the accuracy was up to 0.895, the sensitivity was 0.910, and the specificity was 0.880. Compared with other algorithms, the proposed algorithm has improved accuracy and specificity. It proves that the proposed method effectively improves the robustness and accuracy of heart sound classification and is expected to be applied to machine-assisted auscultation.