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Solutions

Experts to annotate
waveforms at scale

Waveforms are critical indicators of patient health, and can be leveraged to train predictive algorithms that detect heart, lung, neurological conditions, and more.

Our annotation platform enables clients to structure waveforms by both classifying features within the waveform and segmenting regions of interest. 

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Use cases

EEG

Identify sharp waves, spikes or spike-and-wave complexes to understand epilepsy, seizures, stroke or tumor presence.

ECG

Tag patterns (e.g. P waves), size (e.g. T wave amplitude) and time ranges (e.g. QRS complex, R-R intervals) associated with cardiac rhythm disturbances, blood flow, and electrolyte changes.

PPG

Identify fiducial points and noise in the waveform to better understand volumetric changes in blood in peripheral circulation.

Annotation types

Classification
Range selection

Testimonials

Trusted by AI leaders across healthcare

“The Centaur Labs platform provided labels at a scale that was 10x, or 20x, anything we had done by ourselves. Tremendous scale, tremendous throughput, and high quality labels.”

Daniel Barbosa

Machine Learning Engineer


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“Annotations that would have taken me 3 months to complete with our prior data annotation system, Centaur Labs completed in only 2 weeks.”

Eric Olson

Founder and CEO


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"Centaur Labs made it easy and fun to collect data labels that enable our cardiology AI research. They are an ideal partner for collecting troves of opinions in a matter of days."

Dr. Gordon S. Huggins, MD

Cardiology Fellowship Program Director


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Explore

More labeling solutions

Text

  • Unstructured clinical notes

  • Scientific text

  • Chatbots

  • And more

Audio

  • Heart auscultation

  • Lung auscultation

  • Artery auscultation

  • And more

Images

  • Ultrasound

  • External images

  • X-ray

  • And more

Video

  • Surgical

  • Clinical sessions

  • And more

Waveform

  • EEG

  • ECG

  • PPG

  • And more

Accelerate your AI development today.

Accurate and scalable health data labeling and model evaluation

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