AI Framework for ECG Analysis
Overview
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.
Outcome
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.
Service Card
| Category | AI products |
| Domain | Healthcare, Diagnostics, Cardiac Monitoring |
| Vertical | Health and Biotechnology |
| Target users | MedTech Companies, Healthcare Providers, Wearable Device Manufacturers, Clinical Research Organizations |
| Service type | Model / AI Workflow |
| Outputs | Fine-tuned arrhythmia classification model for training; arrhythmia type labels per ECG segment for inference; optional extracted respiratory signal from ECG. |
| Requirements | Requires GPU computing resources for model training. Customer-provided single-lead ECG recordings or use of supported public datasets for model development. |
| Limitations | Limited 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. |
| Availability | Q1/2027 |
| Licensing | MIT and CC BY-NC-SA 4.0 |
| Visual content | assets/service-visuals/ecg-analysis.png |
| Visual caption |