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Synthetic Datasets

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.

Available Datasets

ECG Synthetic

An ECG synthetic dataset generated using PhysioKit. The dataset enables the generation of 12-lead ECG signals with a variety of heart conditions and noise levels along with segmentations and fiducial points.

PPG Synthetic

A PPG synthetic dataset generated using PhysioKit. The dataset enables the generation of a 1-lead PPG signal with segmentations and fiducials.

Usage

Example

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import heartkit as hk

ds = hk.DatasetFactory.get('ecg-synthetic')(
    num_pts=100,
    params=dict(
        sample_rate=1000, # Hz
        duration=10, # seconds
        heart_rate=(40, 120),
    )
)

with ds.patient_data(patient_id=ds.patient_ids[0]) as pt:
    ecg = pt["data"][:]
    segs = pt["segmentations"][:]
    fids = pt["fiducials"][:]

Funding

NA

Licensing

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