Tuija Vasankari

Verified Contributor7 Publications in Hub

Research Bibliography

ID: severe-aortic-stenosis-detection-using-seismocardiography2026

Severe aortic stenosis detection using seismocardiography

This study shows that chest vibrations measured by a small device can accurately detect severe heart valve disease, offering a low-cost alternative to traditional tests like echocardiography.

ID: end-to-end-sensor-fusion-and-classification-of-atrial-fibrillation-using-deep-neural-networks-and-smartphone-mechanocardiography2022

End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography

This study shows that smartphones can detect atrial fibrillation (AFib) using vibrations from the chest with high accuracy, offering a practical and affordable heart monitoring solution.

ID: cardiac-time-intervals-derived-from-electrocardiography-and-seismocardiography-in-different-patient-groups2022

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.

ID: detecting-aortic-stenosis-using-seismocardiography-and-gryocardiography-combined-with-convolutional-neural-networks2021

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.

ID: multiclass-classifier-based-cardiovascular-condition-detection-using-smartphone-mechanocardiography2018

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.

ID: comprehensive-analysis-of-cardiogenic-vibrations-for-automated-detection-of-atrial-fibrillation-using-smartphone-mechanocardiograms2018

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.

ID: machine-learning-based-classification-of-myocardial-infarction-conditions-using-smartphone-derived-seismo--and-gyrocardiography2018

Machine Learning Based Classification of Myocardial Infarction Conditions Using Smartphone-Derived Seismo- and Gyrocardiography

Researchers used smartphone sensors to track heart changes in heart attack patients before and after treatment, achieving promising accuracy with machine learning methods.