University of Extremadura
Avda. de la Universidad, S/N.
1000, Cáceres, Spain
Working for your success
ECGTwinMentor
Interactive ECG Simulation and AI Diagnosis
ECGTwinMentor is an advanced clinical simulation and educational platform designed to enhance the understanding of patterns through predictive modeling and digital twin technology.
The ECGTwinMentor project has been developed by a multidisciplinary team composed primarily of software engineers and healthcare professionals.
Our collaborative effort bridges the gap between clinical knowledge and advanced technological solutions. By integrating expertise in artificial intelligence, biomedical signal processing, and cardiology, we have built a robust educational platform aimed at improving ECG interpretation skills.
- Expertise in AI-powered diagnostic tools
- Strong collaboration nursery students and developers
- Focused on medical education through digital twins
- Agile and research-driven development approach
- Integration of real-world healthcare needs and software design
- Passionate about advancing eHealth innovation
Features
Discover the main features
Clinical relevance
Empowering medical training through realistic ECG simulation and analysis.
- Pathology-aware predictions: Trained on clinically curated datasets, the system provides accurate interpretations of common and critical heart conditions.
- Educational twin modeling: Offers a digital twin that mimics human cardiac behavior, helping students understand ECG dynamics interactively.
- Diagnostic feedback loop: Compares model output with expected clinical findings to reinforce decision-making skills.
Intelligent technology
Leveraging machine learning and secure software to ensure precision and trust.
- AI-driven prediction engine: Uses a deep learning model to forecast potential cardiac anomalies based on user input.
- Secure data processing: Implements AES encryption, rate limiting, and CORS policies to ensure privacy and robustness.
- Edge compatibility: Optimized for deployment on edge devices like Raspberry Pi, enabling offline and portable usage.
User-centric design
Built for educators, students, and healthcare innovators with usability in mind.
- Interactive web interface: Intuitive React-based frontend for entering parameters, visualizing ECGs, and receiving real-time feedback.
- Accessible and responsive: Works seamlessly across devices, adapting to different screen sizes and user needs.
- Scenario-based learning: Users can explore multiple ECG scenarios to simulate varied clinical conditions.
A novel approach to ECG education
ECGTwinMentor redefines medical training by merging AI prediction with digital twin simulation, offering interactive ECG scenarios that go beyond traditional static tools.
Clinically-informed and validated
Developed with input from healthcare professionals and tested against curated datasets, the system ensures medical relevance and reliable diagnostic alignment.
Powered by cutting-edge technology
Using TensorFlow, React, and FastAPI, the platform delivers high-performance predictions, with support for TFLite and ONNX to enable edge and cross-platform deployment.
Committed to data privacy and integrity
With AES encryption, CORS, and rate limiting, ECGTwinMentor safeguards user data while ensuring fast and secure processing—without storing personal health information.
Cross-platform ready
Seamlessly deployable on both cloud infrastructure and edge devices like Raspberry Pi for maximum flexibility and reach.
Modern design
Clean, intuitive interfaces built with React and Bootstrap ensure an engaging and professional user experience.
User-friendly
Minimal learning curve thanks to a guided workflow and contextual feedback that supports both students and educators.
Error-resilient
Robust validation and exception handling mechanisms maintain system stability even under faulty input or network issues.
Responsive layout
Adapts fluidly to desktops, tablets, and smartphones, ensuring usability across a wide range of screen sizes.
Browser-compatible
Tested for compatibility with all major browsers and operating systems to guarantee a consistent experience everywhere.
Experience the future of ECG learning
Discover how intelligent simulation and real-time feedback can transform the way you interpret and understand electrocardiograms.
Request a demo
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Prediction Accuracy
ECG Parameters
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Predictions Generated
Clinical scenarios simulated
Services
A smart, interactive tool that combines clinical insight with real-time prediction and visualization to enhance ECG learning and diagnostic skills
Real-time ECG visualization
Simulate and display ECG waveforms dynamically based on input parameters, offering a visual twin of cardiac activity for enhanced learning.
Instant user feedback
Receive immediate diagnostic predictions and comparisons to clinical standards, helping users quickly assess their understanding and performance.
Modular design & model export
Flexible architecture allows easy updates and export of trained models in .h5, .tflite, and .onnx formats, ready for deployment in various environments.
Usage statistics & insights
Track prediction activity and user interaction through detailed logs and visual dashboards, supporting continuous improvement and monitoring.
Pricing
General plans
Starter Plan
€
-
/ month
For individual learners and early adopters.
Featured Included:
- Access to core ECG simulation features
- Real-time ECG visualization
- Immediate diagnostic feedback
- Limited number of predictions per month
Most Popular
Academic Plan
€
-
/ month
Designed for universities and training centers.
Featured Included:
- All Starter Plan features
- Multi-user classroom access
- Instructor dashboard and progress tracking
- Priority support for academic environments
Professional Plan
€
-
/ month
For hospitals, clinics, and research teams.
Featured Included:
- All Academic Plan features
- Advanced analytics and usage statistics
- Edge deployment support (e.g. Raspberry Pi)
- Dedicated technical support
Have a question? Check out the FAQ
Learn what ECGTwinMentor is, who it’s for, how accurate and secure it is, and how it can be used both online and offline — even integrated into academic programs.
What is ECGTwinMentor used for?
ECGTwinMentor is an AI-powered educational platform designed to simulate ECG signals, provide diagnostic predictions, and support the teaching of cardiology concepts in an interactive way.
Do I need medical knowledge to use the system?
Not necessarily. The platform is designed for both medical students and non-experts. It provides guided inputs, visual feedback, and clinical context to help you learn progressively.
Can I use the tool offline or on local devices?
Yes. ECGTwinMentor supports edge deployment on devices like Raspberry Pi, enabling offline usage in environments with limited internet access.
How accurate are the model predictions?
The prediction engine has been trained on validated ECG datasets and tested with expert-reviewed cases, achieving over 92% accuracy on common diagnostic patterns.
Is my data safe while using the platform?
Absolutely. All input data is encrypted using AES-256, with strict control policies (CORS, rate limiting), and no personal health information is stored.
Can I integrate ECGTwinMentor into my institution’s curriculum?
Yes. The Academic and Professional plans include multi-user access, instructor dashboards, and custom integration support for universities and training centers.
Why choose us?
Combining medical expertise with intelligent technology to redefine ECG education.
ECGTwinMentor stands out by offering a clinically validated, AI-driven platform that’s secure, adaptable, and designed for real-world learning. Whether you’re a student, educator, or healthcare innovator, our solution delivers meaningful insights, instant feedback, and scalable deployment—anywhere, anytime.
ContactContact
We’d love to hear from you.
Whether you're a student, educator, healthcare professional, or institution interested in integrating ECGTwinMentor, feel free to reach out. Our team is ready to provide support, answer your questions, or schedule a personalized demo. Let’s work together to transform ECG education through intelligent technology