https://github.com/nabilalibou/ecg_processor
A small Python class for processing ECG signals and computing heart rates using Fourier transform and peak detection methods (Maxima detection).
https://github.com/nabilalibou/ecg_processor
ecg heart-rate heart-rate-analysis python
Last synced: 3 months ago
JSON representation
A small Python class for processing ECG signals and computing heart rates using Fourier transform and peak detection methods (Maxima detection).
- Host: GitHub
- URL: https://github.com/nabilalibou/ecg_processor
- Owner: nabilalibou
- License: mit
- Created: 2025-04-10T09:05:16.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-10T09:41:28.000Z (6 months ago)
- Last Synced: 2025-06-01T21:47:17.059Z (4 months ago)
- Topics: ecg, heart-rate, heart-rate-analysis, python
- Language: Python
- Homepage:
- Size: 519 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ECG Processor
A Python class for processing ECG signals and computing heart rates using Fourier transform and
peak detection methods (Maxima detection).
## Features
- ECG signal processing:
- Butterworth bandpass filter (0.05-40 Hz)
- Low cutoff of 0.05Hz to remove baseline wander caused by patient breathing or movement artifacts.
- High cutoff of 40 Hz to remove high-frequency noise while preserving the main frequency content of QRS complexes
(10-25 Hz). The noise is generally caused by power line interference (50/60 Hz) and EMG artifacts.
- Heart rate calculation methods:
- Time-domain R-peak detection with adaptive thresholding
- Fourier transform frequency analysis (0.5-2.5 Hz range)
- EDF file support
## Installation
```bash
pip install -r requirements.txt
```
## Usage
1. Place your EDF files in the data folder
2. Run the analysis:
```bash
python main.py
```
## Testing
Run the test suite:
```bash
pytest tests/ -v
```
## Observations about the data
2nd signal '0002.edf': Poor R wave prominence compared to S negative deflection.
3rd signal '0003.edf': Fragmented QRS complexes with some notching on the upstroke of the R wave.
## Future Improvements
- Advanced baseline correction with median filter or polynomial fitting.
- Handle fragmented QRS with smoothing Savitzky-Golay filter or QRS template matching (cross-correlation).
- Implement adaptive peak detection that switches between R and S waves based on prominence metrics.
- Possibility of using prominence-based R wave detection instead of height.
- Signal segmentation for long recordings.
- Signal quality assessment (SNR-based metrics)
- Logging system