Abstract

This paper is to explore the improvement of clinical symptoms in patients with cardiovascular neurosis (CN) by physical exercise based on the deep learning architecture of edge computing, and to deeply explore the effect of physical exercise on autonomic function. Fifty-two patients with CN in this cardiovascular rehabilitation center were randomly divided into drug group and exercise group, with 26 cases in each group, and their electrocardiogram (ECG) was examined. Based on the deep learning architecture of edge computing, a four-layer stacked sparse auto encoder (SSAE) deep neural network was constructed, and the accuracy rates of least squares support vector machine (LSSVM), message passing neural network (MPNN), convolutional neural network (CNN), and SSAEs were measured to be 95.4%, 93.6%, 96.3%, and 99.5%, respectively. After physical exercise intervention, the total score of Symptom Checklist 90 (SCL-90) as well as each single item score were lower in the exercise group than in the drug group ([Formula: see text]). Heart rate recovery (HRR1) improved more significantly after 1[Formula: see text]min of exercise in patients in the exercise group ([Formula: see text]). The low-frequency (LF) power and normalized low-frequency (LFn) power of blood pressure variability (BPV) parameters in the exercise group were lower than those in the drug group ([Formula: see text]); the total power (TP), high-frequency (HF) power, and normalized high-frequency (HFn) power of heart rate variability (HRV) parameters in the exercise group were higher than those in the drug group ([Formula: see text]), LF/HF in the exercise group was lower than that in the drug group ([Formula: see text]); and the baroreflex sensitivity (BRS) in the exercise group was higher than that in the drug group ([Formula: see text]). A four-layer SSAEs was successfully constructed; the mechanism of exercise may be related to the regulation of cardiovascular autonomic nervous function, and it can effectively prevent and treat the clinical symptoms of patients with CN.

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