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.

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.

The PhD student will be part of the Advanced Sleep Monitoring group at department of Electrical Engineering of Eindhoven University of Technology and will closely work with Philips Cardiologs in Paris. The PhD will be based in Eindhoven, with the expectation to visit Philips Cardiologs office in Paris few times per year.

You will work on a daily basis with Tom Mingasson, Fokke van Meulen, Nicola Pezzotti, Pedro Fonseca and Sebastiaan Overeem.

We offer a meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,872 max. €3,670).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

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.

Information

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Application

We invite you to submit a complete application by using the apply button.
The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.
   

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.