physiokit 0.10.1

PhysioKit, process raw ambulatory bio-signals.

A Python toolkit to process raw ambulatory bio-signals.

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Documentation: https://ambiqai.github.io/physiokit

Source Code: https://github.com/AmbiqAI/physiokit

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Key Features:

• Handles a variety of physiological signals including ECG, PPG, RSP, and IMU.
• Geared towards real-time, noisy wearable sensor data.
• Provide advanced signal processing and feature extraction methods.
• Create synthetic signals for testing and benchmarking.

Requirements

• Python 3.11+

Installation

Installing PhysioKit can be done using uv or pip.

pip install physiokit

uv add physiokit

Example

In this example, we will generate a synthetic ECG signal, clean it, and compute heart rate and HRV metrics.

import numpy as np
import physiokit as pk

sample_rate = 1000 # Hz
heart_rate = 64 # BPM
signal_length = 8*sample_rate # 8 seconds

# Generate synthetic ECG signal
ecg, segs, fids = pk.ecg.synthesize(
    signal_length=signal_length,
    sample_rate=sample_rate,
    heart_rate=heart_rate,
    leads=1
)
ecg = ecg.squeeze()

# Clean ECG signal
ecg_clean = pk.ecg.clean(ecg, sample_rate=sample_rate)

# Compute heart rate
hr_bpm, _ = pk.ecg.compute_heart_rate(ecg_clean, sample_rate=sample_rate)

# Extract R-peaks and RR-intervals
peaks = pk.ecg.find_peaks(ecg_clean, sample_rate=sample_rate)
rri = pk.ecg.compute_rr_intervals(peaks)
mask = pk.ecg.filter_rr_intervals(rri, sample_rate=sample_rate)

# Re-compute heart rate
hr_bpm = 60 / (np.nanmean(rri[mask == 0]) / sample_rate)

# Compute HRV metrics
hrv_td = pk.hrv.compute_hrv_time(rri[mask == 0], sample_rate=sample_rate)

bands = [(0.04, 0.15), (0.15, 0.4), (0.4, 0.5)]
hrv_fd = pk.hrv.compute_hrv_frequency(
    peaks[mask == 0],
    rri[mask == 0],
    bands=bands,
    sample_rate=sample_rate
)

License

This project is licensed under the terms of BSD 3-Clause.