  Alterations in Heart Rate (HR) and Heart Rate Variability (HRV) can reflect
autonomic dysfunction associated with neurodegeneration. We investigate the
influence of Mild Cognitive Impairment (MCI) on HR and its variability measures
in the Indian population by designing a complete signal processing pipeline to
detect the R-wave peaks and compute HR and HRV features from ECG recordings of
10 seconds, for point-of-care applications. The study cohort involves 297 urban
participants, among which 48.48% are male and 51.51% are female. From the
Addenbrooke's Cognitive Examination-III (ACE-III), MCI is detected in 19.19% of
participants and the rest, 80.8% of them are cognitively healthy. Statistical
features like central tendency (mean and root mean square (RMS) of the
Normal-to-Normal (NN) intervals) and dispersion (standard deviation (SD) of all
NN intervals (SDNN) and root mean square of successive differences of NN
intervals (RMSSD)) of beat-to-beat intervals are computed. The Wilcoxon rank
sum test reveals that mean of NN intervals (p = 0.0021), the RMS of NN
intervals (p = 0.0014), the SDNN (p = 0.0192) and the RMSSD (p = 0.0206) values
differ significantly between MCI and non-MCI classes, for a level of
significance, 0.05. Machine learning classifiers like, Support Vector Machine
(SVM), Discriminant Analysis (DA) and Naive Bayes (NB) driven by mean NN
intervals, RMS, SDNN and RMSSD, show a high accuracy of 80.80% on each
individual feature input. Individuals with MCI are observed to have
comparatively higher HR than healthy subjects. HR and its variability can be
considered as potential biomarkers for detecting MCI.
