SigClean: A Python Library for Biomedical Signal Cleaning

SigClean is a comprehensive Python library for cleaning and preprocessing biomedical signals including ECG, EMG, EEG, and other physiological signals. It provides a complete toolkit for signal filtering, artifact removal, noise reduction, and signal quality assessment.

Features

Signal Filtering

• Butterworth filters: Lowpass, highpass, bandpass, and bandstop filtering
• Notch filters: Remove power line interference (50/60 Hz)
• Adaptive filtering: LMS adaptive noise cancellation
• Savitzky–Golay filtering: Smooth signals while preserving features
• Moving average: Simple signal smoothing

Preprocessing & Cleaning

• Baseline removal: Linear detrending, polynomial fitting, median filtering
• Artifact removal: Statistical outlier detection and interpolation
• Normalization: Z-score, min–max, robust, and unit vector normalization
• Resampling: Change sampling frequency with interpolation or scipy methods

Analysis & Quality Assessment

• Signal segmentation: Windowed analysis with overlap
• Peak detection: Find R-peaks, spikes, and other signal features
• SNR calculation: Signal-to-noise ratio estimation
• Quality metrics: Comprehensive signal quality assessment

Visualization

• Signal plotting: Time-domain visualization with customization
• Frequency analysis: Power spectral density and spectrograms
• Before/after comparisons: Side-by-side filtering results
• Multi-signal plots: Compare multiple signals simultaneously
• Quality assessment plots: Comprehensive signal quality visualization

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API Reference

Filtering Functions (sigclean.filters)

• butterworth_filter() – Apply Butterworth filters (lowpass, highpass, bandpass, bandstop)
• notch_filter() – Remove specific frequency components (e.g., power line interference)
• baseline_removal() – Remove baseline drift and trends
• artifact_removal() – Detect and remove signal artifacts
• adaptive_filter() – LMS adaptive filtering for noise reduction
• moving_average_filter() – Simple moving average smoothing
• savitzky_golay_filter() – Savitzky–Golay smoothing filter

Utility Functions (sigclean.utils)

• normalize_signal() – Signal normalization (z-score, min–max, robust)
• resample_signal() – Change sampling frequency
• segment_signal() – Split signal into overlapping windows
• calculate_snr() – Signal-to-noise ratio calculation
• detect_peaks() – Peak detection in signals
• remove_outliers() – Outlier detection and removal
• calculate_signal_quality() – Comprehensive quality assessment

Plotting Functions (sigclean.plot)

• plot_signal() – Basic signal visualization
• plot_frequency_spectrum() – Power spectral density plots
• plot_before_after() – Before/after filtering comparison
• plot_multiple_signals() – Multi-signal visualization
• plot_spectrogram() – Time–frequency analysis
• plot_signal_quality_assessment() – Comprehensive quality plots
• plot_filter_response() – Filter frequency response

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Contributing

I welcome contributions!

1. Fork the repository
2. Create a feature branch

   git checkout -b feature/amazing-feature

3. Commit your changes

   git commit -m "Add amazing feature"

4. Push to the branch

   git push origin feature/amazing-feature

5. Open a Pull Request

License

This project is licensed under the MIT License – see the LICENSE file for details.

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Citation

If you use SigClean in your research or projects, please cite it as:

@software{sigclean2024,
  author    = {Diptiman Mohanta},
  title     = {SigClean: A Python Library for Biomedical Signal Cleaning},
  year      = {2025},
  publisher = {GitHub},
  url       = {https://github.com/diptiman-mohanta/sigclean}
}