Matti Kaisti
mkaist@utu.fi +358 44 533 4238 Vesilinnantie 5 Turku ORCID identifier: https://orcid.org/0000-0001-8118-2851 |
Areas of expertise
medical instrumentation, biosignal analytics, clinical machine learning
medical instrumentation, biosignal analytics, clinical machine learning
Biography
Research
I work in biomedical engineering to develop disease monitoring systems. By applying signal processing and AI to novel sensory data, I aim to create clinically validated solutions that integrate technologies across various maturity levels.
Teaching
I currently teach Analytics of Health Wearables, an advanced engineering course focused on programming and algorithms, and Introduction to Health Technology, an entry-level course."
Publications 
3 of 8
- Evaluating Piezoelectric Ballistocardiography for Post-Surgical Heart Rate Monitoring (2024)
- Computing in Cardiology
(A4 Refereed article in a conference publication ) - Generating Synthetic Mechanocardiograms for Machine Learning Based Peak Detection (2024) Sandelin, Jonas; Elnaggar, Ismail; Lahdenoja, Olli; Kaisti, Matti; Koivisto, Tero
(A1 Refereed original research article in a scientific journal) - Hemodynamic Bedside Monitoring Instrument with Pressure and Optical Sensors : Validation and Modality Comparison (2024)
- Advanced Science
(A1 Refereed original research article in a scientific journal) - Investigating the impact of contact pressure on photoplethysmograms (2024)
- Biomedical Engineering Advances
(A1 Refereed original research article in a scientific journal) - Low-Cost Tissue Oximetry Using Discrete Light-Emitting Diodes (2024)
- IEEE International Instrumentation and Measurement Technology Conference
(A4 Refereed article in a conference publication ) - Non-Invasive Hemodynamic Monitoring System Integrating Spectrometry, Photoplethysmography, and Arterial Pressure Measurement Capabilities (2024)
- Advanced Science
(A1 Refereed original research article in a scientific journal) - Parallel, Continuous Monitoring and Quantification of Programmed Cell Death in Plant Tissue (2024)
- Advanced Science
(A1 Refereed original research article in a scientific journal) - Personalization of Affective Models Using Classical Machine Learning : A Feasibility Study (2024)
- Applied Sciences
(A1 Refereed original research article in a scientific journal) - Smartphone-Based Recognition of Heart Failure by Means of Microelectromechanical Sensors (2024)
- JACC: Heart Failure
(A1 Refereed original research article in a scientific journal) - Toward Automatic Cardiovascular and Respiratory Assessment Using Automatic 6-Minute Walking Test (2024)
- Proceedings of IEEE Sensors
(A4 Refereed article in a conference publication )
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