Controlled Quantized Study of Electroacupuncture in Treating Patients with Alzheimer’s Disease Based on Deep Learning Models

Abstract

The incidence of Alzheimer’s Disease (AD) in the world’s elderly over 65 is about 4% to 6%. Recent statistics show that there are nearly 40 million AD patients worldwide. According to the knowledge of neuroscience, there are two main biological indicators for the diagnosis of dementia in the medical community: one is the size of the hippocampus (equivalent to the brain memory chip), and the other is the size of the ventricle. Because the volume of the ventricles increases as the brain tissue degenerates. Because the cause of AD is unknown, it is generally found that it is late, even if the treatment will not have much effect. Therefore, early diagnosis of AD is a better way to inhibit the rapid development of the disease or even avoid the disease. At present, the pathogenesis and etiology of AD have not been fully elucidated, and there is a lack of a specific anti-AD drug. Electroacupuncture treatment of AD has been proven to have a certain effect, and has the advantages of diversified stimulation parameters, easy operation and no toxic side effects. In this paper, we study the role of electroacupuncture based on deep learning in patients with Alzheimer’s disease. The experimental results can prove the effectiveness of the proposed methodology.