Automatic computerized analysis of heart rate variability with digital filtering of ectopic beats

Area of Science:

• Cardiology
• Biomedical Engineering
• Data Science

Background:

• Heart rate variability (HRV) analysis is crucial for assessing autonomic function and cardiovascular risk.
• Ectopic beats, or abnormal heartbeats, can significantly interfere with HRV analysis, leading to inaccurate results.
• Existing HRV analysis methods may not adequately address the impact of ectopic beats.

Purpose of the Study:

• To investigate the impact of ectopic beats on heart rate variability (HRV) analysis.
• To evaluate the effectiveness of a digital filter algorithm in correcting HRV data corrupted by ectopic beats.
• To assess the feasibility of automated HRV analysis in large clinical datasets.

Main Methods:

• Power spectral analysis was performed on synthetic data with varying proportions of ectopic beats.
• 24-hour Holter recordings from 98 healthy subjects and 93 post-myocardial infarction (MI) patients were analyzed.
• Data was analyzed both with and without a digital filtering and interpolation algorithm designed to correct for ectopic beats and noise.

Main Results:

• Even a low proportion (less than 1%) of ectopic beats significantly hampered HRV analysis.
• The filter algorithm effectively corrected for ectopic beats and noise in synthetic data.
• Filtering markedly reduced extraneous variability caused by non-normal beats in both healthy and post-MI patient groups.
• The software enabled automated batch processing of over one hundred 24-hour Holter recordings.

Conclusions:

• HRV analysis requires filtering for ectopic beats, even when they are infrequent.
• The developed filter algorithm provides effective correction for ectopic beats, improving HRV analysis accuracy.
• Automated HRV analysis with filtering is feasible for large-scale clinical and epidemiological studies, enhancing diagnostic capabilities.