These weights are intended for inference on continuous ABP waveforms sampled at 125 Hz. The public inference package segments the waveform into pulse-to-pulse intervals, converts each pulse into a 224x224 single-channel image, and returns artifact probabilities or binary labels.

Inference code: https://github.com/moberg-analytics/oss-models

Files

cnn-resnet18-full-best-v2-6895bffcf59e0153fbae93fe7f3e0b93.pth
cnn-efficientnet_b0-head_only-best-v3-ab40c448fd91102b747244f445e05137.pth

Architecture Details

Public model	Components	Threshold
`okekeclean-abp-ensemble`	ResNet-18 (full FT, weight `0.4`) + EfficientNet-B0 (shallow FT, weight `0.6`)	`0.184`
`okekeclean-abp-resnet18`	ResNet-18 (full FT)	`0.17785164713859558`
`okekeclean-abp-efficientnet_b0`	EfficientNet-B0 (shallow FT)	`0.04994076117873192`

Both CNN backbones replace the classifier head with Linear -> ReLU -> Dropout -> Linear, and the first convolution is adapted to single-channel pulse images.

Performance

Held-out ABP test-set performance from the associated paper:

Model	Accuracy	Sensitivity	Specificity	AU-ROC
Ensemble 1	`0.795`	`0.952`	`0.730`	`0.958`
ResNet-18 (Full FT)	`0.851`	`0.915`	`0.824`	`0.951`
EfficientNet-B0 (Shallow FT)	`0.898`	`0.799`	`0.939`	`0.945`

Training Data

The ABP models were trained on waveforms from the PRECICECAP study:

63 ICU patients across seven sites
5,707 total ABP monitoring hours
10,005 final pulse-level examples after segmentation review
Approximately 30% artifact prevalence

Each pulse was annotated on a 4-point ordinal scale by expert physician reviewers and then binarized into artifact vs non-artifact classes.

Limitations and Biases

Training data comes from a single clinical study population and may not represent all monitor vendors, line setups, or patient populations.
The released operating threshold prioritizes high sensitivity, which lowers specificity relative to some standalone models.
Borderline pulses near the distinction between slight artifact and substantial artifact remain the primary source of error.

Citation

Tony K. Okeke, Manil Shrestha, Ethan Moyer, Karen G. Hirsch, Teresa L May, Zihuai He, Richard Moberg, Jonathan Elmer. Transfer Learning for Artifact Detection in ICU-Collected Arterial Blood Pressure Waveforms. Abstract presented at Neurocritical Care Society (NCS) Annual Meeting, Montreal, QC, Canada. September 2025.
Moberg Analytics OSS Models Repository

bibtex

@inproceedings{okeke2025transfer,
  title        = {Transfer Learning for Artifact Detection in {ICU}-Collected Arterial Blood Pressure Waveforms},
  author       = {Okeke, Tony K. and Shrestha, Manil and Moyer, Ethan and Hirsch, Karen G. and May, Teresa L. and He, Zihuai and Moberg, Richard and Elmer, Jonathan},
  booktitle    = {Neurocritical Care Society (NCS) Annual Meeting},
  year         = {2025},
  month        = sep,
  address      = {Montreal, QC, Canada},
  note         = {Abstract presentation}
}

moberg-analytics/okekeclean-abp

okekeclean-abp-v1

Model Description

Intended Use