Department of Electrical Engineering, National Institute of Technology, Calicut, Kerala, India.
J Med Syst. 2012 Jun;36(3):1935-41. doi: 10.1007/s10916-011-9653-x. Epub 2011 Jan 27.
An automated diagnostic system for diabetes mellitus (DM), from heart rate variability (HRV) measures, using feed forward neural network has been developed. Changes in autonomic nervous system activity caused by DM are quantified by means of time domain and frequency domain analysis of HRV. Electrocardiograms of 70 DM patients and 65 healthy volunteers were recorded. Nine time domain measures-standard deviation of all NN intervals, square root of mean of sum of squares of differences between adjacent NN interval (RMSSD), number of adjacent NN intervals differing more than 50 ms. (NN50 count), percentage of NN50 count, R-R triangular index, triangular interpolation of NN intervals (TINN), standard deviation of the mean heart rate, mean R-R interval and mean heart rate-were used as the input features to the neural network. This diagnostic system classifies DM patients and normal volunteers from morphologically identical ECGs. Diagnostic results show that the system is performing well with an accuracy of 93.08%, specificity of 96.92% and sensitivity of 89.23%.
已经开发出一种使用前馈神经网络从心率变异性（HRV）测量值自动诊断糖尿病（DM）的系统。通过对 HRV 的时域和频域分析来量化 DM 引起的自主神经系统活动的变化。记录了 70 名 DM 患者和 65 名健康志愿者的心电图。九个时域测量值-所有 NN 间隔的标准差、均方根差的 NN 间隔平方和的平方根（RMSSD）、NN50 计数差值大于 50ms 的 NN 间隔数、NN50 计数百分比、R-R 三角指数、NN 间隔的三角插值（TINN）、平均心率的标准差、平均 R-R 间隔和平均心率-被用作神经网络的输入特征。该诊断系统可从形态相同的 ECG 中对 DM 患者和正常志愿者进行分类。诊断结果表明，该系统的准确率为 93.08%，特异性为 96.92%，灵敏度为 89.23%。
