Why the study?
Despite widespread use of heart rate variability across cardiology and other fields, significant standardization problems, technical challenges, and interpretative limitations restrict its clinical utility.
Design
Narrative review
Key result
Heart rate variability assessment faces significant limitations including standardization challenges and artifact sensitivity, which contemporary machine learning methodologies are helping to overcome.
Authors
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HRV assessment warrants cautious clinical interpretation due to standardization and artifact issues; leaves open whether machine learning reliably enhances utility.
This narrative review highlights the inherent limitations of traditional heart rate variability assessment, such as standardization issues and artifact sensitivity, and explores how contemporary machine learning approaches can overcome these challenges to enhance clinical utility.
Arijita Banerjee (2026) conducted a review in Autonomic dysfunction. Heart rate variability (HRV) analysis was evaluated. Heart rate variability assessment faces significant limitations including standardization challenges and artifact sensitivity, which contemporary machine learning methodologies are helping to overcome.