Spectral Analysis
Studies in this Category
Digital Twin-Based Investigation of Seismocardiogram Sensitivity to Tissue Mechanics and Myocardial Motion
This study shows how personalized computer models based on CT scans can simulate heart vibrations (SCG) and improve non-invasive heart monitoring by accounting for individual anatomy and tissue properties.
PulsatioMech: An Open-Source MATLAB Toolbox for Seismocardiography Signal Processing
This study presents a free MATLAB tool that helps researchers analyze heart vibrations (SCG signals) to better understand heart health and develop wearable monitoring devices.
Effect of the Airway Pressure on the Frequency Domain of Seismocardiographic Signal
This study shows how changes in breathing pressure affect heart vibrations, which could help monitor heart muscle health in the future.
Advanced Fusion and Empirical Mode Decomposition-Based Filtering Methods for Breathing Rate Estimation from Seismocardiogram Signals
This study shows how heart vibration signals can be used to estimate breathing rate accurately without invasive procedures, using advanced signal processing techniques like EMD and fusion methods.
Detecting Aortic Valve-Induced Abnormal Flow with Seismocardiography and Cardiac MRI
This research shows that chest vibrations measured by SCG can detect abnormal heart valve flow, offering a quick and affordable way to identify heart issues compared to MRI scans.
Comparison of multiple cardiac signal acquisition technologies for heart rate variability analysis
This study shows that a new sensor technology, PiPG, can measure heart rate variability almost as accurately as an ECG, making it a promising tool for monitoring heart health in various settings.
Seismocardiography and 4D flow MRI reveal impact of aortic valve replacement on chest acceleration and aortic hemodynamics
This study shows how chest vibration measurements and advanced MRI can track improvements in blood flow after heart valve surgery, offering a quick and affordable way to monitor recovery.
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.
Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients
This study shows that a wearable device can track heart failure severity by analyzing chest vibrations during exercise, potentially helping doctors monitor patients remotely and adjust treatments effectively.
Amplitude Modulation Effects in Cardiac Signals
This study shows how to better analyze heart signals by using simple techniques to reveal hidden patterns, which could improve heart monitoring methods.