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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- Two-stage Classification for Detecting Murmurs from Phonocardiograms Using Deep and Expert Features (2022)
- Computing in Cardiology
- CardioSignal Smartphone Application Detects Atrial Fibrillation in Heart Failure Population (2021)
- Circulation
- Control Method for Continuous Non-Invasive Arterial Pressure Monitoring using the Non-Pulsatile Component of the PPG Signal (2021)
- Computing in Cardiology
- Detecting Aortic Stenosis Using Seismocardiography and Gryocardiography Combined with Convolutional Neural Networks (2021)
- Computing in Cardiology
- Miniaturization of a Finger-Worn Blood Pressure Instrument (2021)
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Sleep During Menopausal Transition: A 10-year Follow-Up (2021)
- Sleep
- Sleep during menopausal transition: a 10-year follow-up (vol 40, pg , 2021) (2021)
- Sleep
- The Effects of External Pressure on Multi-Wavelength Photoplethysmography Signals (2021)
- Computing in Cardiology
- An instrument for measuring blood pressure and assessing cardiovascular health from the fingertip (2020)
- Biosensors and Bioelectronics
- Classification of Atrial Fibrillation and Acute Decompensated Heart Failure Using Smartphone Mechanocardiography: A Multi-label Learning Approach (2020)
- IEEE Sensors Journal
UTU Research Portal