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AI Framework for ECG Analysis

Opis

This service enables automated detection and classification of cardiac arrhythmias from single-lead ECG recordings. It helps healthcare providers and medtech companies integrate reliable cardiac screening into wearable devices, remote patient monitoring platforms, and clinical workflows---reducing manual interpretation effort and accelerating diagnosis. The service builds on the ECGDL open-source deep learning framework, which integrates nine diverse ECG datasets with state-of-the-art neural network architectures and a validated inter-patient evaluation protocol ensuring fair generalization across patient populations. It also supports ECG-derived respiration extraction using signal processing and deep learning, enabling combined cardio-respiratory monitoring from a single sensor.

Pričakovani rezultati

A trained arrhythmia detection model tailored to the customer's device or clinical application, along with arrhythmia classification results on customer-provided ECG recordings. Optionally, respiratory signal extraction from ECG data for integrated cardio-respiratory monitoring.

Kartica storitve

KategorijaAI products
DomenaHealthcare, Diagnostics, Cardiac Monitoring
VertikalaHealth and Biotechnology
Ciljni uporabnikiMedTech Companies, Healthcare Providers, Wearable Device Manufacturers, Clinical Research Organizations
Tip storitveModel / AI Workflow
RezultatFine-tuned arrhythmia classification model for training; arrhythmia type labels per ECG segment for inference; optional extracted respiratory signal from ECG.
ZahteveRequires GPU computing resources for model training. Customer-provided single-lead ECG recordings or use of supported public datasets for model development.
OmejitveLimited to single-lead ECG data. Model performance depends on recording quality and representativeness of the training population. Not a certified medical service---intended for research and decision-support applications.
Na voljo odQ1/2027
LicencaMIT and CC BY-NC-SA 4.0
Slikaassets/service-visuals/ecg-analysis.png
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