Tero Koivisto

Verified Contributor10 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: smartphone-based-recognition-of-heart-failure-by-means-of-microelectromechanical-sensors2024

Smartphone-Based Recognition of Heart Failure by Means of Microelectromechanical Sensors

This study shows that smartphones can detect heart failure with high accuracy using built-in motion sensors, offering a simple and non-invasive way to monitor heart health remotely.

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.

ID: heart-rate-variability-estimation-with-joint-accelerometer-and-gyroscope-sensing2016

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.

ID: a-new-algorithm-for-segmentation-of-cardiac-quiescent-phases-and-cardiac-time-intervals-using-seismocardiography2015

A new algorithm for segmentation of cardiac quiescent phases and cardiac time intervals using seismocardiography

This study shows how chest vibrations can measure heart mechanics and identify resting phases of the heart, which could improve imaging and early disease detection without expensive equipment.