Synthetic Data

Overview

By leveraging physioKIT, we are able to generate synthetic data for a variety of physiological signals, including ECG, PPG, and respiration. In addition to the signals, the tool also provides corresponding landmark fiducials and segmentation annotations. While not a replacement for real-world data, synthetic data can be useful in conjunction with real-world data for training and testing the models.

Please visit physioKIT for more details.

Funding

NA

Licensing

The tool is available under BSD-3-Clause License.

Integration

Usage

Python

Python example

import physiokit as pk

heart_rate = 64 # BPM
sample_rate = 1000 # Hz
signal_length = 10*sample_rate # 10 seconds

# Generate NSR synthetic ECG signal
ecg, segs, fids = pk.ecg.synthesize(
    signal_length=signal_length,
    sample_rate=sample_rate,
    heart_rate=heart_rate,
    leads=1,
    preset=pk.ecg.EcgPreset.NSR,
    p_multiplier=1.5,
    t_multiplier=1.2,
    noise_multiplier=0.2
)

Synthetic signal generation is a standalone preprocessing tool, not a registered sleepKIT dataset. To use generated signals in a task, provide a custom dataset adapter and matching feature extraction.