Multi-Band Analysis

Multi-Band Analysis#

Decompose an EGG signal across the three gastric frequency bands (bradygastria, normogastria, tachygastria) using multiband_analysis. Each band gets its own narrowband filter, phase extraction, and metrics.

import matplotlib.pyplot as plt
import numpy as np

import gastropy as gp

plt.rcParams["figure.dpi"] = 100
plt.rcParams["figure.facecolor"] = "white"

egg = gp.load_egg()
best_idx, _, _, _ = gp.select_best_channel(egg["signal"], egg["sfreq"])
best_signal = egg["signal"][best_idx]
sfreq = egg["sfreq"]
times = np.arange(len(best_signal)) / sfreq

Run Multi-Band Analysis#

results = gp.multiband_analysis(best_signal, sfreq)

print(f"{'Band':<12} {'Peak (Hz)':<11} {'Peak (cpm)':<12} {'N cycles':<10} {'IC':<10} {'Power %':<10}")
print("-" * 65)
for name, res in results.items():
    print(
        f"{name:<12} {res['peak_freq_hz']:<11.4f} {res['peak_freq_hz'] * 60:<12.2f} "
        f"{res['cycle_stats']['n_cycles']:<10} "
        f"{res['instability_coefficient']:<10.4f} {res['prop_power']:<10.1%}"
    )

Band         Peak (Hz)   Peak (cpm)   N cycles   IC         Power %   
-----------------------------------------------------------------
brady        0.0300      1.80         10         1.0639     7.3%      
normo        0.0530      3.18         38         0.1470     76.0%     
tachy        0.0960      5.76         72         0.2275     6.9%

Per-Band Filtered Signals#

colors = {"brady": "#E74C3C", "normo": "#27AE60", "tachy": "#2E86AB"}

fig, axes = plt.subplots(3, 1, figsize=(14, 7), sharex=True)
for ax, (name, res) in zip(axes, results.items(), strict=True):
    filt = res["filtered"]
    if not np.all(np.isnan(filt)):
        ax.plot(times, filt, lw=0.7, color=colors[name])
    band = res["band"]
    ax.set_title(f"{name.capitalize()} ({band['f_lo'] * 60:.1f}–{band['f_hi'] * 60:.1f} cpm)")
    ax.set_ylabel("Amplitude")
    ax.spines["top"].set_visible(False)
    ax.spines["right"].set_visible(False)
axes[-1].set_xlabel("Time (seconds)")
fig.tight_layout()
plt.show()
../_images/ceb7e8dd3404cffa6cf5bd5fe122a06c2a4469d8cd775abc6338fc032b49edb2.png

Power Distribution Across Bands#

names = list(results.keys())
powers = [results[n]["prop_power"] for n in names]

fig, ax = plt.subplots(figsize=(6, 4))
bars = ax.bar(names, powers, color=[colors[n] for n in names], edgecolor="white")
ax.set_ylabel("Proportion of Total Power")
ax.set_title("Power Distribution Across Gastric Bands")
for bar, p in zip(bars, powers, strict=True):
    ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.01, f"{p:.1%}", ha="center", fontsize=10)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
plt.show()
../_images/08d48ebc6f902433d04988a6893cd08db3b08e91e5d5cd7b160d9cb53d635603.png

See also: PSD Visualization, egg_process

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