okekeclean 1.0.1

Artifact detection for physiological signals (ABP and ECG)

OkekeClean

OkekeClean provides pre-trained artifact detection models for arterial blood pressure (ABP) and electrocardiogram (ECG) waveforms. The package ships inference code for the released models and downloads checkpoint weights from Hugging Face on first use.

Installation

pip install okekeclean

Or with uv:

uv add okekeclean

Quick Start

ABP

import pandas as pd
import torch

from okekeclean import ABPParams, detect_artifacts, load_model

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = load_model("okekeclean-abp-ensemble", device=device)

waveform = pd.Series(
    [80.0, 83.2, 90.5, 102.1, 110.4, 105.0, 96.3, 88.7] * 500,
    index=pd.Timestamp("2025-01-01")
    + pd.to_timedelta(range(4000), unit="s") / 125,
)

artifact_flags = detect_artifacts(
    waveform=waveform,
    modality="ABP",
    model=model,
    params=ABPParams(),
    device=device,
)

ECG

import numpy as np
import pandas as pd
import torch

from okekeclean import ECGParams, detect_artifacts, load_model

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = load_model("okekeclean-ecg-efficientnet_b0", device=device)

samples = 5000
time = np.arange(samples) / 500.0
waveform = pd.Series(
    0.9 * np.sin(2 * np.pi * 1.2 * time) + 0.05 * np.random.randn(samples),
    index=pd.Timestamp("2025-01-01") + pd.to_timedelta(time, unit="s"),
)

artifact_probs = detect_artifacts(
    waveform=waveform,
    modality="ECG",
    model=model,
    params=ECGParams(),
    device=device,
    return_type="probability",
)

See quickstart_abp.py and quickstart_ecg.py for runnable examples with synthetic inputs.

Released Models

Model	Signal	Architecture	Threshold	Test performance
okekeclean-abp-ensemble	ABP	ResNet-18 (full FT) + EfficientNet-B0 (shallow FT)	0.184	AU-ROC 0.958, sensitivity 0.952, specificity 0.730, accuracy 0.795
okekeclean-abp-resnet18	ABP	ResNet-18 (full FT)	0.17785164713859558	AU-ROC 0.951, sensitivity 0.915, specificity 0.824, accuracy 0.851
okekeclean-abp-efficientnet_b0	ABP	EfficientNet-B0 (shallow FT)	0.04994076117873192	AU-ROC 0.945, sensitivity 0.799, specificity 0.939, accuracy 0.898
okekeclean-ecg-efficientnet_b0	ECG	EfficientNet-B0 (full FT)	0.20802117884159088	AU-ROC 0.970, sensitivity 0.858, specificity 0.950, accuracy 0.922

Input Requirements

• ABP: pd.Series with a DatetimeIndex, sampled at 125 Hz, units in mmHg.
• ECG: pd.Series with a DatetimeIndex, sampled at 500 Hz, units in mV. ECG segments are resampled to 500 Hz internally if needed.

Model Weights

• ABP weights: https://huggingface.co/moberg-analytics/okekeclean-abp
• ECG weights: https://huggingface.co/moberg-analytics/okekeclean-ecg

Papers

• ABP Paper
• ECG Paper

License

GNU Affero General Public License v3. See LICENSE.

Citation

@misc{okekeclean,
  title = {OkekeClean},
  author = {Tony Kabilan Okeke},
  year = {2026},
  howpublished = {\url{https://github.com/moberg-analytics/oss-models}}
}