Xavier Anjo, Noble Sneha, Joseph Justin, Ghosh Aishwarya, Issac Thomas Gregor
Centre for Brain Research, Indian Institute of Science, Bengaluru, India.
Am J Alzheimers Dis Other Demen. 2024 Jan-Dec;39:15333175241309527. doi: 10.1177/15333175241309527.
Alterations in Heart Rate (HR) and Heart Rate Variability (HRV) reflect autonomic dysfunction associated with neurodegeneration making them biomarkers suitable for detecting Mild Cognitive Impairment (MCI). The study involves 297 urban Indian participants [48.48% (144) were male and 51.51% (153) were female]. MCI was detected in 19.19% (57) of participants and the rest, 80.8% (240) of them were healthy. ECG recordings spanning 10 s were collected and R-peaks were detected. Machine learning algorithms like were employed to further validate the features. The mean of R-to-R (NN) intervals ( = .0021), the RMS of NN intervals ( = .0014), the SDNN ( = .0192) and the RMSSD ( = .0206) values differ significantly between MCI and non-MCI. Machine learning classifiers, SVM, DA, and NB show a high accuracy of 80.801% on RMS feature input. HR and its variability can be considered potential biomarkers for detecting MCI.
心率（HR）和心率变异性（HRV）的改变反映了与神经退行性变相关的自主神经功能障碍，这使得它们成为适用于检测轻度认知障碍（MCI）的生物标志物。该研究涉及297名印度城市参与者[48.48%（144名）为男性，51.51%（153名）为女性]。19.19%（57名）的参与者被检测出患有MCI，其余80.8%（240名）为健康人。收集了持续10秒的心电图记录并检测出R波峰。使用了诸如机器学习算法来进一步验证这些特征。MCI组和非MCI组之间的R-R（NN）间期平均值（=0.0021）、NN间期均方根（=0.0014）、标准差（SDNN，=0.0192）和相邻NN间期差值的均方根（RMSSD，=0.0206）值存在显著差异。机器学习分类器支持向量机（SVM）、判别分析（DA）和朴素贝叶斯（NB）在均方根特征输入上显示出80.801%的高精度。心率及其变异性可被视为检测MCI的潜在生物标志物。
