Matti Kaisti
mkaist@utu.fi +358 44 533 4238 Vesilinnantie 5 Turku |
medical instrumentation, biosignal analytics, clinical machine learning
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.
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."
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- Wearable edge machine learning with synthetic photoplethysmograms (2024)
- Expert Systems with Applications
- Advances in non-invasive blood pressure measurement techniques (2023)
- IEEE Reviews in Biomedical Engineering
- Continuous Blood Pressure Monitoring Using Nonpulsatile Photoplethysmographic Components for Low-Frequency Vascular Unloading (2023)
- IEEE Transactions on Instrumentation and Measurement
- Development and clinical validation of a miniaturized finger probe for bedside hemodynamic monitoring (2023)
- iScience
- Domain randomization using synthetic electrocardiograms for training neural networks (2023)
- Artificial Intelligence in Medicine
- Enhancing the Reliability of Wearable Cardiac Monitoring using Accelerometer Activity Data (2023)
- Body Sensor Networks Conference
- Method for measuring jugular venous pulse with a miniature gyroscope sensor patch (2023)
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Tonometric Multi-Wavelength Photoplethysmography for Studying the Cutaneous Microvasculature of the Fingertip (2023)
- IEEE Transactions on Instrumentation and Measurement
- Cardiac Time Intervals Derived from Electrocardiography and Seismocardiography in Different Patient Groups (2022)
- Computing in Cardiology
- Multichannel Bed Based Ballistocardiography Heart Rate Estimation Using Continuous Wavelet Transforms and Autocorrelation (2022)
- Computing in Cardiology
UTU Research Portal