Heart Rate and its Variability From Short-Term ECG Recordings as Potential Biomarkers for Detecting Mild Cognitive Impairment.

American journal of Alzheimer's disease and other dementias Pub Date : 2024-01-01 DOI:10.1177/15333175241309527

Anjo Xavier, Sneha Noble, Justin Joseph, Aishwarya Ghosh, Thomas Gregor Issac

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Abstract

Background: 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). Methods: 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. Results: The mean of R-to-R (NN) intervals (P = .0021), the RMS of NN intervals (P = .0014), the SDNN (P = .0192) and the RMSSD (P = .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. Conclusion: HR and its variability can be considered potential biomarkers for detecting MCI.

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将短期心电图记录的心率及其变异性作为检测轻度认知障碍的潜在生物标志物

背景：心率（HR）和心率变异性（HRV）的改变反映了与神经变性相关的自主神经功能障碍，使它们成为检测轻度认知障碍（MCI）的生物标志物。方法：研究纳入297名印度城市参与者[男性144人，占48.48%；女性153人，占51.51%]。57例（19.19%）被检出MCI，其余240例（80.8%）为健康者。采集10 s的心电记录，检测r峰。机器学习算法被用来进一步验证这些特征。结果：MCI与非MCI的R-to-R （NN）区间均值（P = 0.0021）、NN区间均方根（P = 0.0014）、SDNN （P = 0.0192）和RMSSD （P = 0.0206）值差异有统计学意义。机器学习分类器、SVM、DA和NB在RMS特征输入上的准确率高达80.801%。结论：HR及其变异性可作为MCI检测的潜在生物标志物。

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American journal of Alzheimer's disease and other dementias

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