Sharanya Senthamilselvan, Manthan Maheshwari, Sridhar P Arjunan, Dinesh K Kumar, Mona Duggal
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引用次数: 0
摘要
心脏自主神经病变(CAN)是糖尿病(DM)的一种急性并发症,在亚临床阶段不会表现出明显症状。研究人员已经开发出多种技术,证明使用软件工具进行分类效率更高。当分类边界不匹配时,使用硬件进行同样的诊断就会失败,因为有更多机会误读类别。在这项研究中,我们通过调查正常 CAN (N) 和早期 CAN (E) 分类的错误率,在使用软件进行复杂性分析和使用控制板在硬件中进行验证之间引入了转化研究。研究显示,在 CAN 诊断的特定分段中,RR 和 ST 的错误率更高(12±8%)。相比之下,PR 和 QT 在分形维度 (FD) 值的软件和硬件实施之间显示出较小的误差百分比(6±4 %)。
A translational study for detection of cardiac autonomic neuropathy using fractal features: A bench to bedside approach.
Cardiac autonomic Neuropathy (CAN) is an acute complication of Diabetes mellitus (DM) that does not exhibit overt symptoms in the subclinical stage. Researchers have developed several techniques that have proved to give higher efficiency in classification using software tools. The challenge in implementing the same using hardware for diagnosis fails when classification boundaries are mismatched, as there are more chances of misinterpreting the classes. In this study, we have introduced translational research between the complexity analysis using software and verifying the same by deploying it in hardware using a controller board by investigating the error percentage in classifying normal (N) and early CAN (E). The study reveals that among the segments specific to CAN diagnosis, RR and ST show more error percentages (12±8 %). In contrast, PR and QT show a lesser error percentage (6±4 %) between software and hardware implementation of Fractal dimension (FD) values.