Key result
Heart rate variability features from 10-second ECG recordings differed significantly between MCI and healthy participants, with machine learning classifiers achieving 80.8% diagnostic accuracy.
Why the study?
Alterations in heart rate and heart rate variability reflect autonomic dysfunction associated with neurodegeneration, making them potential biomarkers for detecting mild cognitive impairment.
Do heart rate and its variability from short-term ECG recordings accurately detect mild cognitive impairment in urban Indian participants?
Cross-Sectional (n=297)
Do heart rate and its variability from short-term ECG recordings accurately detect mild cognitive impairment in urban Indian participants?
p-value: p=.0014
Short-term ECG-derived heart rate variability features, combined with machine learning, show potential as non-invasive biomarkers for detecting mild cognitive impairment.
Short ECG HRV metrics may flag MCI in community samples; hypothesis-generating and requires prospective validation before any clinical role.
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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Xavier et al. (2024) conducted a cross-sectional in Mild Cognitive Impairment (n=297). Heart rate and heart rate variability from short-term ECG vs. Healthy controls was evaluated on Difference in RMS of NN intervals between MCI and non-MCI (p=.0014). Heart rate variability features from 10-second ECG recordings differed significantly between MCI and healthy participants, with machine learning classifiers achieving 80.8% diagnostic accuracy.
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