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PhysioKit, process raw ambulatory bio-signals.

Project description

PhysioKit

A Python toolkit to process raw ambulatory bio-signals.

Package version Supported Python versions Package downloads Package downloads License

🚧 PhysioKit is under active development


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

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


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

Installation

Installing PhysioKit can be done using Poetry or pip.

pip install physiokit
poetry add physiokit

Example

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

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(
    duration=10,
    sample_rate=sample_rate,
    heart_rate=heart_rate,
    leads=1
)

# 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.

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