Leveraging Dataframe-Based Operations for Calculation of Heart Rate Variability

This paper introduces a novel Heart Rate Variability (HRV) calculation strategy, diverging from traditional divide-and-conquer methodologies to dataframes utilizing the Polars library as an advanced data manipulation tool. We demonstrate significant improvements in computational performances, where the dataframe approach outperforms the iterative approach with speedup factors beyond 98 times for short-term HRV calculations and substantial reductions in processing times across various test cases. These enhancements underscore the potential of tailored dataframe manipulations in enhancing performance and adapting to complex data analysis challenges in HRV assessments.