MSc thesis project proposal

Improving signal quality of a wearable device for electrocardiogram (ECG) measurement

Project outside the university

Praxa Sense
The field “Personal Healthcare” is a rapidly expanding area of biomedical engineering. More people are using wearable medical devices in their domestic environment rather than in a clinical setting. One of the widely used bioelectrical signals in healthcare monitoring is electrocardiogram (ECG). ECG is used in the diagnosis and evaluation of cardiovascular diseases.

An ECG is usually measured with Ag/AgCl electrodes in direct contact with the body surface by using electronic devices such as Analog Front-End (AFE) devices. However, using a wearable medical device in a domestic environment comes with difficulties. Simple body motions, walking, cycling, etc. affect the ECG signal quality. The goal of this study is to find a signal processing method to reduce noise on a single lead ECG signal with an AFE device placed near the heart containing two electrodes.

Praxa Sense is a medical tech start-up company in Delft currently working on developing Afi: An unobtrusive long-term monitoring device that will enable the early detection of the most common heart rhythm diseases of this time. Heart rhythm diseases are hard to recognize due to the nature of the symptoms. The general practitioner has little low-threshold detection tools at hand. We will bridge this gap by enabling early diagnosis with a user-friendly device. By detecting heart rhythm diseases early, serious consequences, like strokes, can be prevented.


  1. Literature study
  2. Algorithm development for ECG resolution improvement. Improve signal to noise ratio. Optimize P-wave signal capturing.
  3. Simulation and testing using synthesized and real data (will be provided by Praxa sense).
  4. Report writing
  5. (optional) If time permits: algorithm development for detecting atrial fibrillation


  1. courses on Signals & systems
  2. Matlab and/or python. (python preferred)

Contact Alle-Jan van der Veen

Signal Processing Systems Group

Department of Microelectronics

Last modified: 2019-05-01