A Novel Adaptive Noise Elimination Algorithm in Long RR Interval Sequences for Heart Rate Variability Analysis

Area of Science:

• Cardiology
• Biomedical Engineering
• Signal Processing

Background:

• Heart rate variability (HRV) analysis is crucial in clinical practice.
• Distorted RR interval (RRI) data acquisition is a common source of errors in HRV studies.
• Existing noise elimination methods may alter the temporal integrity of RRI sequences.

Purpose of the Study:

• To develop an artifact elimination algorithm for long-term RRI sequences.
• To ensure the algorithm preserves the overall structure and duration of the RRI data.
• To enhance the accuracy of HRV analysis, particularly in polygraphy.

Main Methods:

• An original adaptive smart time series step-by-step analysis was employed.
• Statistical verification methods were integrated into the algorithm.
• The algorithm was designed for long-term (hours or days) RRI sequences.

Main Results:

• The developed adaptive algorithm effectively eliminates artifacts from RRI data.
• The algorithm preserves the temporal structure and total duration of the RRI sequence.
• Reconstruction of the heart-rate structure is maximized by the adaptive approach.

Conclusions:

• The proposed artifact elimination algorithm is suitable for long-term RRI sequences.
• This method minimizes errors in HRV analysis without compromising data integrity.
• The algorithm and its implementation are available for clinical and research use.