Olli Lahdenoja
Dr. Sc. (Tech)
Biomedical Engineering; Computer Vision
Olli Lahdenoja received the M.Sc. and D.Sc. (Tech) degrees from the University of Turku, Finland, in 2003 and 2015, respectively, where he is currently a Senior Researcher in the Health Technology group, Department of Computing, Faculty of Technology. His research interests include biomedical signal processing, biomedical engineering and computer vision. He has also worked at several research projects related to a concrete implementation of embedded systems related to these areas. He has published several international peer-reviewed journal and conference articles. He is currently working part-time at Precordior Ltd.
Biomedical engineering:
- Seismocardiography (SCG)
- Gyrocardiography (GCG)
- Ballistocardiography (BCG)
- Electrocardiography (ECG)
- Clinical trials: atrial fibrillation (AF), heart failure (HF), coronary artery disease (CAD)
Computer vision:
- Local binary patterns (LBP)
- Face recognition
- Real-time machine vision (including industrial)
Dr. Sc. Thesis: "Local Binary Patterns in Focal-Plane Processing - Analysis and Applications" (2015)
Analog IC design / Mixed-Mode IC design (2005-2010)
- Cardiac monitoring of dogs via smartphone mechanocardiography: a feasibility study (2019)
- BioMedical Engineering OnLine
- Comprehensive Analysis of Cardiogenic Vibrations for Automated Detection of Atrial Fibrillation Using Smartphone Mechanocardiograms (2019)
- IEEE Sensors Journal
- Embedded processing methods for on-line visual analysis of laser welding (2019)
- Journal of Real-Time Image Processing
- Head Pulsation Signal Analysis for 3-Axis Head-Worn Accelerometers (2019)
- Computing in Cardiology
- Reliability of Self-Applied Smartphone Mechanocardiography for Atrial Fibrillation Detection (2019)
- IEEE Access
- Stand-alone Heartbeat Detection in Multidimensional Mechanocardiograms (2019)
- IEEE Sensors Journal
- Atrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone (2018)
- IEEE Journal of Biomedical and Health Informatics
- Machine Learning Based Classification of Myocardial Infarction Conditions Using Smartphone-derived Seismo- and Gyrocardiography (2018)
- Computing in Cardiology
- MOBILE PHONE DETECTION OF ATRIAL FIBRILLATION: THE MODE-AF STUDY (2018)
- Journal of the American College of Cardiology
- Mobile Phone Detection of Atrial Fibrillation With Mechanocardiography The MODE-AF Study (Mobile Phone Detection of Atrial Fibrillation) (2018)
- Circulation



