Matti Kaisti

Verified Contributor5 Publications in Hub

Research Bibliography

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: 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: 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.