PhysioKit, process raw ambulatory bio-signals.
Project description
A Python toolkit to process raw ambulatory bio-signals.
🚧 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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