Robust Estimation of Inter-beat Interval Skewness for Autonomic Dysregulation Assessment in Noisy Neonates ECG
Talithea Concepción · University of South Carolina
Advisors: Dr. Christian O’Reilly & Dr. Jessica Bradshaw
This repository contains the full analysis pipeline for extracting ECG-derived features such as skewness and entropy from neonatal ECG recordings.
The goal is to evaluate how inter-beat interval (IBI) skewness varies with window length and noise level, validating it as a potential biomarker for autonomic dysregulation and early ASD detection.
Neonatal ECG signals are often short and noisy, limiting traditional long-segment analyses.
By computing skewness and entropy over multiple smaller segments and applying bootstrapping, this project:
- Quantifies stability vs. window size (1–5 minutes)
- Measures bias introduced by segment length
- Produces reproducible KDE and variance plots
src/ecgasd/ # Core feature extraction and signal utilities ├─ notebooks/ # Analysis notebooks ├─ configs/ # YAML configuration files ├─ docs/ # Figures, HTML exports, and TSV outputs ├─ scripts/ # Reproducibility and automation scripts ├─ tests/ # Unit tests ├─ data/ # (git-ignored) Raw / interim ECG data └─ environment.yml # Reproducible environment definition