Tero Hurnanen
Research Bibliography
Cardiac Time Intervals Derived from Electrocardiography and Seismocardiography in Different Patient Groups
This study shows that heart function can be monitored using vibrations from the chest and ECG, offering a simpler alternative to ultrasound for tracking changes after heart valve surgery.
Detecting Aortic Stenosis Using Seismocardiography and Gryocardiography Combined with Convolutional Neural Networks
This study shows that heart vibrations measured by wearable sensors and analyzed with AI can detect aortic stenosis with over 98% accuracy, offering a simpler alternative to traditional echocardiography.
Multiclass Classifier based Cardiovascular Condition Detection Using Smartphone Mechanocardiography
This study shows that smartphones can detect heart conditions like AFib and heart attacks using built-in sensors and machine learning, offering a promising tool for global heart health monitoring.
Comprehensive Analysis of Cardiogenic Vibrations for Automated Detection of Atrial Fibrillation Using Smartphone Mechanocardiograms
This study shows that a smartphone can detect atrial fibrillation (AFib) with high accuracy using chest vibrations, making heart monitoring accessible and easy for everyone without extra devices.
Heart Rate Variability Estimation with Joint Accelerometer and Gyroscope Sensing
This study shows how combining accelerometer and gyroscope sensors can improve heart rate variability tracking, paving the way for better wearable heart monitors.