Bachelor and Master Theses

Background:

Biomedical signal processing is a critical component in modern healthcare, extracting valuable insights from physiological signals like the Photoplethysmogram (PPG). PPG, a non-invasive optical technique, provides information about blood volume changes and monitors various aspects of health, including cardiovascular conditions. Its potential lies in early detection and continuous monitoring of diseases.

In healthcare, AI and machine learning have gained prominence for revolutionizing disease diagnosis, predictive modeling, and risk assessment. These technologies enhance the accuracy and efficiency of cardiovascular disease diagnosis by analyzing PPG data. Real-time data processing is vital in healthcare, allowing immediate responses to changes in a patient's condition. In the context of cardiovascular health, real-time analysis enables early anomaly detection, timely medical interventions, and potential life-saving measures, reducing healthcare costs.

Embedded systems, such as the Raspberry Pi, are compact, cost-effective solutions for real-time data acquisition and processing, serving as a vital link between the Spo2 finger clip device and the AI-based predictive modeling system, making them integral to this project.

Motivation :

The primary motivation driving this project is the development of a real-time system for signal processing and AI-based predictive modeling, applied specifically to healthcare monitoring. This endeavor seeks to create a platform that can provide immediate insights into a patient's health status, enabling timely interventions and continuous monitoring.  Real-time analysis of physiological signals, such as PPG data, offers the promise of enhancing patient care and ensuring informed decision-making in healthcare settings.

Task:

1. Data Preprocessing and Feature Engineering
2. Spo2 Finger Clip Device Integration
3. Real-time Signal Processing and AI Modeling

References:

• Abdullah S, Hafid A, Folke M, Lindén M and Kristoffersson A (2023) PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points. Front. Bioeng. Biotechnol. 11:1199604. doi: 10.3389/fbioe.2023.1199604
• S. Abdullah, A. Hafid, M. Lindén, M. Folke and A. Kristoffersson, "Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters," 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), L'Aquila, Italy, 2023, pp. 923-924, doi: 10.1109/CBMS58004.2023.00344.
• The 2023 wearable photoplethysmography roadmap. DOI 10.1088/1361-6579/acead2

Knowledge of Python or Matlab, Signal processing, AI, and Machine learning. A basic understanding of biosignals is beneficial.