TY -的A2 - M。Bhagyaveni盟——黄,Jiong盟——见鬼,PY - 2022 DA - 2022/01/12 TI -分析慢性肺心病的诱导因素引起的慢性阻塞性肺疾病通过流行病学调查在高海拔下智能医学和大数据SP - 2612074六世- 2022 AB -本研究探讨慢性肺心病的风险因素(CPHD)引起高原慢性阻塞性肺疾病(COPD)基于智能医疗和大数据的心电图(ECG)信号。介绍了基于GPU,小波算法提取心电信号的特征,并结合广义回归神经网络(GRNN)来提高分类精度。从2018年6月到2020年12月,在高原地区10185例诊断为COPD肺功能测试,心电图,胸透X医院作为研究对象来评估CPHD发病率在不同年龄段的分布和高度。GTX780Ti的运行时间短的15倍左右是比CPU。N检测的准确性基于GPU-accelerated神经网络模型达到了98.06%。准确性(Acc), (Se)的敏感性,特异性(Sp),和积极的速度V (PR)是99.03%,89.17%,98.92%,和93.18%,分别。Acc, Se、Sp和公关的年代是99.54%,86.22%,99.74%,和92.56%,分别。GRNN分类准确率高达98%。慢性阻塞性肺病患者被诊断出患有CPHD的19%,其中包括1409名男性(72.82%)和526名女性(36.24%)。 The highest prevalence of CPHD was 64.60% when the altitude was 1,900–2,499 m, and the prevalence was only 2.43% when the altitude was ≥3,500 m. The highest prevalence of CPHD was 63.77% at the age of 61–70 years, and the lowest prevalence at the age of 15∼20 years was only 0.26%. Therefore, the GPU-based neural network model improved the classification accuracy of ECG signals. Age and altitude were risk factors for CPHD induced by high-altitude COPD, which provided a reference for the prevention, diagnosis, and treatment of CPHD in high-altitude areas. SN - 2040-2295 UR - https://doi.org/10.1155/2022/2612074 DO - 10.1155/2022/2612074 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -