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Personal profile

Academic background

Tom Bakkes received his bachelor’s degree in Electrical Engineering in 2016. He continued his studies in Electrical Engineering for his master’s degree and specialized in the signals processing systems group. This is where he got interested in using the techniques he had learned in a Care & Cure setting. For his graduation project he joined the Biomedical Diagnostics lab where he worked on using machine learning for predicting the outcome of in-vitro fertilization treatments. Following that he received his master’s degree in 2018 and has now continued working in the Biomedical Diagnostics lab as a PhD candidate focusing on clinical decision support systems in perioperative care.

Research profile

Tom Bakkes is a PhD candidate in the Biomedical Diagnostics lab at the Eindhoven University of Technology. Here he works on using statistics and machine learning to build clinical decision support systems for the perioperative care. Thereby his current studies focus on automatic detection of patient-ventilation asynchrony, and early detection of deterioration in post-operative patients. For the latter study he works in close collaboration with clinical experts from the Catharina hospital in Eindhoven.


I work on clinical decision support systems in the perioperative care that will allow clinicians to achieve a more accurate and faster insight on their patients.

Fingerprint Dive into the research topics where Tom H.G.F. Bakkes is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Thermopiles Engineering & Materials Science
thermopiles Physics & Astronomy
Learning systems Engineering & Materials Science
Ventilation Engineering & Materials Science
Classifiers Engineering & Materials Science
Intensive care units Engineering & Materials Science
Support vector machines Engineering & Materials Science
Monitoring Engineering & Materials Science

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Research Output 2019 2019

  • 1 Conference contribution
  • 1 Poster
  • 1 Article
3 Downloads (Pure)

Machine learning for classification of uterine activity outside pregnancy

Bakkes, T., Sammali, F., Kuijsters, N., Turco, S., Rabotti, C., Schoot, B. & Mischi, M., 7 Oct 2019, Engineering in Medicine and Biology Society (EMBC), Annual International Conference of the IEEE . Piscataway: IEEE EMBS, p. 2161-2164 8857374

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Learning systems
Support vector machines

Towards modelling of patient-ventilator interactions using model based methods

van Diepen, A., Bakkes, T. H. G. F. & Woerlee, P. H., 28 Nov 2019.

Research output: Contribution to conferencePosterAcademic

Open Access
Intensive care units
Learning systems
82 Downloads (Pure)

Unobtrusive respiratory flow monitoring using a thermopile array: a feasibility study

Lorato, I., Bakkes, T., Stuijk, S., Meftah, M. & de Haan, G., 15 Jun 2019, In : Applied Sciences. 9, 12, 15 p., 2449.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access

Student theses

Machine learning for classification of uterine activity during IVF cycles

Author: Bakkes, T., 13 Dec 2018

Supervisor: Mischi, M. (Supervisor 1), Sammali, F. (Supervisor 2) & Schoot, B. (Supervisor 2)

Student thesis: Master