ECG project UMCU

2020

The correct interpretation of electrocardiograms (ECGs) is crucial for accurately diagnosing cardiac abnormalities. Current methods, both manual by physicians and computerized, have not achieved the level of accuracy comparable to cardiologists in detecting acute cardiac issues. Leveraging advancements in artificial intelligence and big data, particularly deep neural networks, offers promising avenues to improve ECG interpretation where traditional methods have fallen short. The ECG-Project develops deep learning algorithms to automate ECG interpretation, particularly focusing on areas where current methods are inadequate.

Through this research, we aim to revolutionize ECG interpretation, improving diagnostic accuracy, reducing healthcare resource utilization, and ultimately enhancing patient outcomes.

The Research
The project objectives are:

  1. WP1: Creating an algorithm capable of accurately and swiftly triaging ECGs through transfer learning, uncovering features in diseases with unknown ECG characteristics (such as primary arrhythmia syndromes and genetic disorders).
  2. WP2: design a portable multi-lead ecg-device, suitable for use by patients at home and healthcare professionals. This device will enable high-quality ECG acquisitions for rapid diagnosis.

Origin
This project is funded within the Innovative Medical Devices Initiative (IMDI) program 'Heart for Sustainable Care'. The focus of this program is the development of medical technology for the earlier detection, monitoring, and better treatment of cardiovascular diseases to ensure accessible healthcare and sufficient staffing. The program has been developed and funded by the Dutch Heart Foundation, ZonMw and NWO, who collaborate within the Dutch CardioVascular Alliance.

Read More

Collaborators

Contact person:

Principal investigators

Read more

PREDICT 2

2019
Sudden cardiac arrest (SCA) remains a significant public health challenge, accounting for nearly 20% of all deaths in developed nations and approximately half of all heart disease-related fatalities. A notable subset of SCA cases occurs in individuals without prior heart disease diagnosis, resulting in profound psychosocial impacts on affected families and society. Ventricular fibrillation (VF) is the primary arrhythmia leading to SCA, often occurring outside healthcare settings with survival rates ranging from 5% to 20%. Prevention is crucial, yet gaps in our understanding of SCA causes and mechanisms hinder effective prevention efforts. Various genetic and non-genetic factors, such as gender, age, comorbidities, and lifestyle, likely influence SCA risk, but their specific contributions remain unclear. The Focus The PREDICT2 initiative brings together leading Principal Investigators with expertise in epidemiology, clinical studies, genetics, and functional research to elucidate factors contributing to SCA, uncover underlying mechanisms, and develop strategies for prevention and treatment. The Research Building on foundational work from PREDICT1, which involved extensive patient characterization and preclinical model development, PREDICT2 focuses on inherited arrhythmia syndromes as models to understand the arrhythmogenic substrate in more common cardiac syndromes associated with SCA. Specifically, PREDICT2 aims to: Identify genetic and non-genetic factors that contribute to SCA risk and develop personalized risk prediction algorithms for individual patient assessment. Conduct functional studies to elucidate the mechanisms underlying SCA, enabling the development of novel risk stratification and therapeutic approaches. Implement clinical studies to evaluate risk prediction algorithms and therapeutic interventions, aiming to enhance the treatment and prevention of SCA. Origin This consortium was funded through the Impulse Grant program by the Dutch Heart Foundation.
Learn more

HEROES

2020
The focus of this project is to develop a novel home-based exergaming system aimed at enhancing resistance to falls among individuals post-stroke. Preventing falls and fall-related injuries, minimizes healthcare utilization and societal costs and supports stroke survivors in maintaining independence in daily life. The Research The HEROES system is designed to target balance perturbations and improve stepping responses. It utilizes action observation and motor imagery techniques to personalize training for individuals with stroke. Stroke survivors will undergo a single training session in a rehabilitation center to practice recovering from real balance perturbations before using HEROES at home. The effectiveness of the HEROES-system will be assessed through a proof-of-principle randomized controlled trial (RCT) involving 60 stroke survivors, evaluating its impact on fall resistance and balance enhancement post-stroke. The approach of involving stroke survivors sets HEROES clearly apart from the currently available home-based exergames, which uses ‘healthy’ people and lack the required personalization of different post-stroke individuals. Origin This project is funded within the Innovative Medical Devices Initiative (IMDI) program 'Heart for Sustainable Care'. The focus of this program is the development of medical technology for the earlier detection, monitoring, and better treatment of cardiovascular diseases to ensure accessible healthcare and sufficient staffing. The program has been developed en funded by the Dutch Heart Foundation, ZonMw and NWO, who collaborate within the Dutch CardioVascular Alliance.
Learn more
1 2 3 20

Looking for
Another item?

Back to overview
Newsletter
© 2024 Oscar Prent Assurantiën BV 
© 2025 | DCVA
Design & Bouw door: