The overarching mission of the Digital Health and Signal Science Axis is to develop structuring tools that enable earlier diagnosis, more precise risk prediction, and more efficient clinical workflows. By combining expertise in data sciences, applied mathematics, laboratory experiments, medtech, and software development, we aim to improve patient outcomes and reduce unnecessary procedures. Our work supports major clinical goals, including the optimization of remote healthcare and cath lab procedures, improved functionality and safety of implantable medical devices, risk stratification for sudden cardiac death, stroke prevention, and more effective use of 12-lead ECGs in both clinical and ambulatory settings.
Signal Processing: Extracting Hidden Insights from Complex Cardiac Data
Our major research area is to develop advanced signal processing algorithms that transform both contact and non-contact high-resolution electro-anatomic cardiac data into clinically meaningful insights. Our approach integrates laboratory experimentation to uncover the mechanisms underlying diverse cardiac phenomena and to validate the performance of our algorithms. We also leverage real-world clinical datasets to ensure the translational relevance of our work. Our signal processing methods draw from applied mathematics, data-driven modeling, and machine learning. We tackle complex challenges such as detecting arrhythmic risk markers, quantifying repolarization heterogeneity, and characterizing atrial conduction patterns relevant to stroke prediction. These tools are designed to support both real-time clinical decision-making and retrospective analysis, enabling improved risk stratification, device optimization, and the advancement of precision cardiology.
Clinical Translation: Translating Technology to Patient Benefit
A key focus of our research is to integrate our novel signal processing tools into real-world clinical care pathways. Working closely with hospital partners and clinical researchers, we apply our technologies to streamline diagnostics and therapeutic decision-making. Efforts include using digital signal analysis for risk stratification for stroke prevention, improved identification of ablation targets to reduce cath-lab procedure times and reducing unnecessary testing by enhancing the diagnostic yield of standard 12-lead ECGs. By ensuring clinical relevance from the earliest stages of development, this team ensures our tools are not only innovative, but also practical and impactful.
Telecardiology: Enabling Remote and Equitable Access to Cardiac Care
Telecardiology is pioneering the future of remote healthcare by developing and validating algorithms that can interpret cardiac signals in decentralized settings. This includes designing AI-assisted diagnostic support tools for home-based follow-up of implantable cardiac devices, and telemonitoring solutions that can be integrated into national healthcare infrastructures. Our goal is to empower clinicians with real-time, accurate information, reducing unnecessary hospital visits and enabling earlier interventions, especially in rural or underserved areas.
Bioengineering Platform: Bridging Experimental Science and Medical Technology
The Bioengineering Platform serves as a cornerstone of medtech development, offering a unique environment to prototype and test new technologies. This includes the development of novel cardiac catheter, systems for in vitro evaluation of new technology, and novel tools simulating physiological conditions for clinical training of EP mapping studies. The innovations of this platform are grounded in both clinical needs and engineering rigor, accelerating their integration into the broader ecosystem.
