Implementation and verification of an enhanced algorithm for the automatic computation of RR-interval series derived...

Area of Science:

• Cardiology
• Biomedical Engineering
• Data Science

Background:

• Electrocardiogram (ECG) analysis from long-term ambulatory recordings is crucial for diagnosing cardiac dysfunction.
• Heart rate variability (HRV) analysis is a key tool for assessing cardiac health and predicting cardiovascular events, especially in end-stage renal disease (ESRD).

Purpose of the Study:

• To verify an enhanced, fully automated algorithm for computing RR-interval time series from multi-lead ECG data.
• To assess the accuracy and reliability of the automated algorithm against manually reviewed reference RR-time series.

Main Methods:

• Utilized 24-hour 12-lead ECG recordings from 339 ESRD patients in the ISAR study.
• Employed multi-lead corrected R-peaks for RR-series computation.
• Verified the algorithm by comparing automated results with manually reviewed reference RR-time series using seven universal indicators.

Main Results:

• The automated algorithm demonstrated high accuracy, with a median intraclass correlation coefficient (indicator of synchronization) of 96.4%.
• The median root mean square error of the difference time series was 7.5 ms, indicating negligible error.
• High synchronization and low error confirmed strong agreement between the automated and reference RR-interval time series.

Conclusions:

• The AIT Multi-Lead ECGsolver provides a verified, fully automated method for generating accurate RR-interval time series.
• This automated approach facilitates reliable HRV analysis for improved cardiac health assessment.
• Future longitudinal studies can evaluate the clinical benefits of HRV parameters derived from this automated RR-time series.