34 citations · 35 across the 5 of their papers we have counts for
5 papers
Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification
Yangyang Zhao, Matti Kaisti, Olli Lahdenoja +1
Atrial fibrillation (AF) is a leading cause of stroke and mortality, particularly in elderly patients. Wrist-worn photoplethysmography (PPG) enables non-invasive, continuous rhythm…
Lightweight ResNet-Based Deep Learning for Photoplethysmography Signal Quality Assessment
Yangyang Zhao, Matti Kaisti, Olli Lahdenoja +10
With the growing application of deep learning in wearable devices, lightweight and efficient models are critical to address the computational constraints in resource-limited platfo…
Flexible framework for generating synthetic electrocardiograms and photoplethysmograms
Katri Karhinoja, Antti Vasankari, Jukka-Pekka Sirkiä +3
By generating synthetic biosignals, the quantity and variety of health data can be increased. This is especially useful when training machine learning models by enabling data augme…
Empirical investigation of multi-source cross-validation in clinical ECG classification
Tuija Leinonen, David Wong, Antti Vasankari +4
Traditionally, machine learning-based clinical prediction models have been trained and evaluated on patient data from a single source, such as a hospital. Cross-validation methods…
Training neural networks with synthetic electrocardiograms
Matti Kaisti, Juho Laitala, Antti Airola
We present a method for training neural networks with synthetic electrocardiograms that mimic signals produced by a wearable single lead electrocardiogram monitor. We use domain ra…