RR-APET

RR-APET performs heart rate variability (HRV) analysis and R-peak detection to quantify time-domain, frequency-domain, and nonlinear HRV metrics from ECG recordings.

Key Features:

• Modular Design: Structured as a modular system offering modules for established R-peak detection algorithms and an embedded template to develop custom algorithms for specific ECG signals or datasets.
• Implemented in Python: The software is developed in the Python programming language.
• Supported Data Formats: Accepts text files, HDF5, MATLAB, and Waveform Database (WFDB) file formats.
• R-peak Detection Algorithms: Includes multiple established R-peak detection algorithm modules and supports user-developed detectors via the provided template.
• Comprehensive HRV Metrics: Quantifies HRV using time-domain, frequency-domain, and nonlinear-domain measures.
• Detection Performance Metrics: When R-peak annotations are available, computes positive predictivity, sensitivity, detection error rate, and accuracy to evaluate R-peak detection algorithms.
• Batch Processing: Supports batch processing for analysis of multiple signals or datasets.

Scientific Applications:

• Clinical HRV assessment: Quantifies HRV metrics used as indicators of patient autonomic and cardiovascular status in clinical research.
• Algorithm evaluation: Enables performance assessment and comparison of R-peak detection algorithms using annotated ECG datasets.
• Personalized medicine research: Facilitates analysis of individual differences in HRV to inform personalized treatment planning.
• HRV–health outcome studies: Supports research into correlations between HRV analytics and healthcare outcomes.

Methodology:

Implemented in Python as a modular system that applies established or user-developed R-peak detection modules, computes time-, frequency-, and nonlinear-domain HRV metrics, calculates positive predictivity, sensitivity, detection error rate, and accuracy when annotations exist, accepts text/HDF5/MATLAB/WFDB formats, and supports batch processing.