Heart Rate and Its Variability From Short-Term ECG Recordings As Potential Biomarkers for Detecting Mild Cognitive Impairment

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.