PhD on Sleep Apnea assessment from ECG

at  TU Eindhoven

JOB DESCRIPTION

Are you inspired by signal processing and deep learning for medical applications? This project is aimed at sleep apnea, one of the most prevalent sleep disorders, and the impact that it has on cardiovascular health. The goal is to develop and validate new algorithms to assess sleep disordered breathing conditions using Holter ECG recordings, and the impact that it has on treatment of cardiovascular conditions.
In recent years, research has shown the impact of sleep disturbances on cardiovascular health, particularly in the context of cardiac arrhythmias such as atrial fibrillation (AF). The interplay between sleep and cardiac function is increasingly recognized, with emerging evidence suggesting bidirectional relationships between sleep disturbances and the onset, progression, and management of cardiovascular diseases.
It is practically impossible to refer every cardiac patient to a formal in-depth sleep examination using gold-standard tools such as polysomnography. However, these patients often undergo (extended) Holter recordings as part of their routine clinical examination. This provides a chance to use these monitoring modalities as surrogate indicators suitable for screening sleep apnea and to provide indications of severity and/or clinical relevance.

This project aims to further develop, optimize, and validate algorithms to assess sleep apnea condition using Holter ECG recordings in cardiac populations. The specific goals of the PhD project are to:

• Improve the diagnostic performance of ECG-based algorithms for sleep apnea detection. Starting from existing models described in the literature and developed by Philips Cardiologs and the Advanced Sleep Monitoring group at TU/e, we will leverage large clinical datasets that combine ECG data with expert annotations of sleep and respiratory events.
• Extend the models’ capabilities, ensuring robustness in the presence of arrhythmias, distinguishing between obstructive and central apnea, and uncovering sleep-stage-specific patterns.
• Investigate translating these algorithms to the Philips Holter recording device (ePatch) using clinical data collected throughout the project.
• Exploit the large Cardiologs Holter databases to study the relationship between cardiac conditions and sleep apnea.

Data to support the project has already been collected: over 2,000 patient records have been annotated by sleep experts, and more will be gathered along the project. Also, there will be the unique opportunity to leverage the Cardiologs Holter datasets, which includes about 3 million extended Holter recordings with annotated cardiac events.
The envisioned research will not only aim at furthering the knowledge in the field, but it is expected to make an impact in the clinic during the duration of the project. To this end, engineers, data scientist and clinicians will be part of the supervisory team and work closely with the student to support the development of new models and support the fast translation of the envisioned research to a clinical setting.

JOB REQUIREMENTS

The candidate should have:

• A Master’s Degree in Computer Science, Electrical or Biomedical Engineering or equivalent.
• A research-oriented attitude with a pro-active mindset.
• Able to interact in a multidisciplinary team including clinical and industrial stakeholders.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).
• Ability to convey results in a clear and rigorous way, and interest in publishing.
• Strong experience in deep learning.
• Excellent coding skills in Python.
• Affinity with clinical topics.

ABOUT US

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.
Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video here.

Please refer the Job description for details