gastropy.egg_clean

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gastropy.egg_clean#

gastropy.egg_clean(data, sfreq, low_hz=None, high_hz=None, method='fir', band=None, **filter_kwargs)[source]#

Clean an EGG signal by applying a bandpass filter.

By default, filters to the normogastric band (0.033-0.067 Hz, 2-4 cpm). Custom frequency bounds can be provided.

Supports three named method variants following the neurokit2 convention of attributable, citable pipelines:

  • "fir" (default) — Zero-phase FIR bandpass with adaptive tap count. Suitable for most applications.

  • "iir" — Zero-phase IIR Butterworth bandpass using cascaded second-order sections (SOS). Faster for real-time or iterative use, slightly less precise roll-off.

  • "dalmaijer2025" — Full preprocessing pipeline from Dalmaijer (2025): Hampel spike removal → LMMSE movement-artifact attenuation → IIR Butterworth bandpass. Recommended when motion artifacts are present in ambulatory recordings.

Parameters:

  • data (array_like) – Raw EGG signal (1D array).

  • sfreq (float) – Sampling frequency in Hz.

  • low_hz (float, optional) – Lower passband edge in Hz. If None, uses band.f_lo.

  • high_hz (float, optional) – Upper passband edge in Hz. If None, uses band.f_hi.

  • method (str, optional) – Cleaning method: "fir" (default), "iir", or "dalmaijer2025".

  • band (GastricBand, optional) – Gastric band for default frequency limits. Default is NORMOGASTRIA.

  • **filter_kwargs – Additional arguments passed to apply_bandpass when method is "fir" or "iir" (e.g., f_order, transition_width). For "dalmaijer2025", accepts hampel_k (int, default 3), hampel_n_sigma (float, default 3.0), and movement_window (float, default 1.0).

Returns:

  • cleaned (np.ndarray) – Cleaned EGG signal.

  • info (dict) – Processing metadata, including "cleaning_method" and sub-step information for multi-stage pipelines.

See also

egg_process

Full EGG processing pipeline.

hampel_filter

Sliding-window spike removal.

remove_movement_artifacts

LMMSE movement artifact filter.

References

Dalmaijer, E. S. (2025). electrography v1.1.1. esdalmaijer/electrography

Gharibans, A. A., Smarr, B., Kunkel, D. C., Kriegsfeld, L. J., Mousa, H., & Coleman, T. P. (2018). Artifact rejection methodology enables continuous, noninvasive measurement of gastric myoelectric activity in ambulatory subjects. Scientific Reports, 8, 5019.

Examples

>>> import numpy as np
>>> from gastropy.egg import egg_clean
>>> sig = np.random.randn(3000)  # 300s at 10 Hz
>>> cleaned, info = egg_clean(sig, sfreq=10.0)

Use the Dalmaijer 2025 full preprocessing pipeline:

>>> t = np.arange(0, 300, 0.1)
>>> sig = np.sin(2 * np.pi * 0.05 * t) + 0.1 * np.random.randn(len(t))
>>> cleaned, info = egg_clean(sig, sfreq=10.0, method="dalmaijer2025")
>>> info["cleaning_method"]
'dalmaijer2025'
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