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)
- Generating Synthetic Mechanocardiograms for Machine Learning Based Peak Detection (2024) Sandelin, Jonas; Elnaggar, Ismail; Lahdenoja, Olli; Kaisti, Matti; Koivisto, Tero
- Mechanocardiography detects improvement of systolic function caused by resynchronization pacing (2023)
- Physiological Measurement
- Cardiac Time Intervals Derived from Electrocardiography and Seismocardiography in Different Patient Groups (2022)
- Computing in Cardiology
- Mechanocardiography-Based Measurement System Indicating Changes in Heart Failure Patients during Hospital Admission and Discharge (2022)
- Sensors
- Mechanocardiography in the Detection of Acute ST Elevation Myocardial Infarction: The MECHANO-STEMI Study (2022)
- Sensors
- Multichannel Bed Based Ballistocardiography Heart Rate Estimation Using Continuous Wavelet Transforms and Autocorrelation (2022)
- Computing in Cardiology
- CardioSignal Smartphone Application Detects Atrial Fibrillation in Heart Failure Population (2021)
- Circulation
- Detecting Aortic Stenosis Using Seismocardiography and Gryocardiography Combined with Convolutional Neural Networks (2021)
- Computing in Cardiology
- Classification of Atrial Fibrillation and Acute Decompensated Heart Failure Using Smartphone Mechanocardiography: A Multi-label Learning Approach (2020)
- IEEE Sensors Journal
- Atrial Fibrillation Detection Using MEMS Accelerometer Based Bedsensor (2019)
- Computing in Cardiology
