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Andrew P. Blaber

Verified Contributor9 Publications in Hub

Research Bibliography

ID: mechanical-deconditioning-of-the-heart-due-to-long-term-bed-rest-as-observed-on-seismocardiogram-morphology2022

Mechanical deconditioning of the heart due to long-term bed rest as observed on seismocardiogram morphology

This study shows how prolonged bed rest weakens the heart and stiffens arteries, using chest vibrations measured by SCG. It suggests SCG could help monitor heart health in space and hospitals with simple wearable devices.

ID: detecting-coronary-artery-disease-using-rest-seismocardiography-and-gyrocardiography2021

Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography

This study shows that chest vibrations measured by a wearable sensor can detect heart disease with high accuracy, offering a potential at-home screening tool for coronary artery disease.

ID: comparison-of-different-methods-for-estimating-cardiac-timings-a-comprehensive-multimodal-echocardiography-investigation2019

Comparison of Different Methods for Estimating Cardiac Timings: A Comprehensive Multimodal Echocardiography Investigation

This study shows that chest vibrations (SCG) can measure heart function more accurately than traditional methods, paving the way for wearable heart monitors.

ID: identifying-patients-with-coronary-artery-disease-using-rest-and-exercise-seismocardiography2019

Identifying Patients With Coronary Artery Disease Using Rest and Exercise Seismocardiography

This study shows that heart vibrations measured during rest and exercise can detect coronary artery disease as accurately as advanced imaging tests, offering a cheaper and faster alternative for diagnosis.

ID: determining-the-respiratory-state-from-a-seismocardiographic-signal--a-machine-learning-approach2018

Determining the Respiratory State From a Seismocardiographic Signal--A Machine Learning Approach

This study shows that chest vibrations from the heart (SCG signals) can predict breathing patterns using advanced machine learning, with neural networks being the most accurate method. This could help monitor breathing and heart health more easily and affordably.

ID: challenges-in-using-seismocardiography-for-blood-pressure-monitoring2017

Challenges in Using Seismocardiography for Blood Pressure Monitoring

This study explored using heart vibrations and pulse signals to estimate blood pressure but found inconsistent results, showing the method needs improvement before clinical use.

ID: accurate-and-consistent-automatic-seismocardiogram-annotation-without-concurrent-ecg2015

Accurate and consistent automatic seismocardiogram annotation without concurrent ECG

This study developed a method to analyze heart vibrations without needing ECG data, showing promise for affordable and standalone heart monitoring devices.

ID: a-3d-model-of-the-thorax-for-seismocardiography2015

A 3D model of the thorax for seismocardiography

This study creates a 3D model of the chest to better understand heart vibrations, helping doctors use SCG for heart health monitoring.

ID: estimating-cardiac-stroke-volume-from-the-seismocardiogram-signal2010

Estimating Cardiac Stroke Volume from the Seismocardiogram Signal

This study shows that heart vibrations measured on the chest (SCG) can estimate the amount of blood pumped by the heart (stroke volume) almost as accurately as ultrasound methods, using machine learning techniques.