ECG Feature Extraction

This Python package provides a set of tools for analyzing electrocardiogram (ECG) signals. It includes functions for signal decomposition using wavelet transform, R-peak detection, extraction of PQRS complexes, heart rate calculation, and various feature extractions. These features would then be saved inside a data frame that can be used for machine learning purposes.

Installation

Download the ECG_Feature_Extraction.py and import functions that you might need.

Install the necessary dependencies using pip:

pip install -r requirements.txt

Dependencies

This package relies on the following libraries:

NeuroKit2

NeuroKit2 is a Python library for neurophysiological signal processing. It provides functions for processing ECG signals, including R-peak detection and heart rate analysis. For more information, visit the NeuroKit2 GitHub repository.

PyWavelets (pywt)

PyWavelets is a Python library for wavelet transform computations. It is used in this package for signal decomposition and analysis. For more information, visit the PyWavelets GitHub repository.

Usage

import numpy as np
import pandas as pd
from ecg_analysis_toolkit import *

# Sample ECG signal data (replace this with your own data)
ecg_signal = np.random.rand(1000)

# Combine 12-lead signals into a single combined signal
weights = {'lead1': 1, 'lead2': 1, 'lead3': 2, 'lead4': 2, 'lead5': 2, 'lead6': 1,
           'lead7': 1, 'lead8': 1, 'lead9': 1, 'lead10': 1, 'lead11': 1, 'lead12': 1}
combined_signal = combine_12_leads(weights, ecg_signal)

# Perform wavelet analysis
plot_wavelet_analysis(ecg_signal, wavelet='sym4', level=3)
reconstructed_signal = wavelet_analysis(ecg_signal, wavelet='sym4', level=3)

# R peak detection
R_peaks, R_peak_values = R_finder(reconstructed_signal)
R_plot(reconstructed_signal)

# Extraction of PQRS complexes
r_peaks, r_amplitudes, p_peaks, p_amplitudes, q_peaks, q_amplitudes, s_peaks, s_amplitudes = PQRS_extraction(ecg_signal)

# Heart rate calculation
heart_rate = HR_counter(ecg_signal)

# Mean PQRS amplitude calculation
mean_r, mean_p, mean_q, mean_s = mean_PQRS_amplitude(ecg_signal)

# Wave duration calculation
r_wave_duration, p_wave_duration, pr_interval = wave_duration(ecg_signal)

# RR ratio calculation
rr_ratio = RR_ratio(ecg_signal)

# Feature extraction
features_df = extraction(combined_signal)

Example Notebook

For an example of how to use these functions to analyze ECG signals from the PTB_XL database, please refer to the provided Jupyter notebook in this repository. This notebook was built upon the data provided by the physio.net database. For the PTB_XL dataset, please visit the PhysioNet website.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

This project is licensed under the Apache License 2.0

Soroushsrd / ECG_Feature_Extraction