Roadmap for Respiratory Rate Estimation from ECG

[dcajal]

Roadmap for Respiratory Rate Estimation from ECG

This roadmap outlines the implementation of methods necessary for respiratory rate estimation from ECG signals. The project involves both ECG-specific methods and general-purpose tools that can be used in other contexts.

Project Structure

• src/ecg/: Contains ECG-specific methods for signal processing and analysis
• src/tools/: Contains general-purpose signal processing tools that can be used across different contexts

Implementation Plan

The workflow for respiratory rate estimation follows these steps:

1. ECG Detection → 2. Baseline Removal → 3. Derivative Filtering → 4. ECG-Derived Respiration (EDR) → 5. Time-Frequency Analysis → 6. Dominant Frequency Estimation

ECG-Specific Methods (src/ecg/)

• ECG detections with Pantomkins (already implemented in ecg/pantompkins.m)
• Baseline removal from ECG signals (already implemented in tools/baselineremove.m)
• ECG-derived respiration method using slope range analysis (already implemented in ecg/sloperange.m)

General-Purpose Tools (src/tools/)

These methods are designed to be versatile and can be used in various signal processing contexts beyond ECG analysis:

• Low-pass derivative filter for signal preprocessing (already implemented in tools/lpdfilter.m)
• Slicing tool [sliced, t] = slicesignal(x, window, overlap, fs)
  • Returns a matrix with the sliced signal ready to use for time-frequency estimation (e.g., using periodogram or pwelch)
• Spectral peakedness calculation pxxPeackedness = peakedness(pxx)
  • Can accept single PSD or matrix of PSDs (from time-frequency maps)
  • Measures how "peaked" or concentrated the spectrum is
• Dominant frequency estimation df = dominantfrequency(pxx, pxxPeackedness)
  • Uses peakedness measure to determine estimation strategy
  • Can work with single PSD or time-frequency matrix