Time Frequency Analysis
Studies in this Category
Multi-site cardiac rhythm monitoring via multi-channel SCG system and exercise-induced physiological analysis
This research developed a system to monitor heart vibrations at multiple chest locations, showing how exercise changes heart valve timing. It could help detect heart issues without invasive tests.
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.
Non-Invasive Wearable Technology to Predict Heart Failure Decompensation
This study reviews wearable devices like smartwatches and patches that monitor heart and lung health to predict worsening heart failure. These technologies could help doctors intervene earlier and prevent hospitalizations, but more research is needed to make them reliable and easy to use.
Investigating Seismocardiogram Patterns: A Computational Modeling of Cardiac Wall Motion Propagation to the Chest Surface
This study uses advanced modeling to simulate heart vibrations on the chest, helping improve non-invasive heart monitoring methods like SCG.
Enhancing visual seismocardiography in noisy environments with adaptive bidirectional filtering for Cardiac Health Monitoring
This study presents a new method to clean heart vibration signals for wearable devices, making heart monitoring more accurate even during movement, without needing traditional ECG wires.
Non-contact heart vibration measurement using computer vision-based seismocardiography
This study shows that a smartphone camera can measure heart vibrations as accurately as traditional sensors, paving the way for affordable heart monitoring at home.
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.
Detecting Preload Changes Using Seismocardiography
This study shows that chest vibration signals (SCG) can detect heart changes caused by increased blood volume, which could help monitor heart failure in clinical settings.
Detection of heart rate using smartphone gyroscope data: a scoping review
This study reviews how smartphone gyroscopes can measure heart rate, showing promise but needing better methods and standards for accuracy and usability in real-life scenarios.
Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases
This study shows that heart vibrations measured by chest sensors (SCG and GCG) can reliably track heart rate variability, even in patients with heart valve diseases, offering a simpler alternative to traditional ECG tests.
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.
Comparison of Heart Rate Variability Indices Based on Seismocardiograms from Healthy Volunteers and Patients with Valvular Heart Diseases
This research shows that heart vibrations measured from the chest can help detect differences in heart rate patterns between healthy people and those with heart valve diseases, offering a new way to monitor heart health outside clinics.
Quantifying preload alterations using a sensitive chest-mounted accelerometer
This study shows that chest vibrations measured by a sensitive sensor can track heart function changes caused by fluid infusion, offering a new way to monitor heart health remotely.
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.
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.
Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms on Healthy Volunteers
This study shows that heart vibrations measured with simple sensors can reliably track heart rate variability, even in patients with heart valve diseases, making heart monitoring more accessible and affordable.
Comparison of Seismocardiography Based Heart Rate Measurement Method
This study shows that using advanced signal processing techniques, like jerk analysis, can make heart rate monitoring with chest vibrations more accurate, offering a simpler alternative to traditional methods like ECG.
Performance Analysis of Gyroscope and Accelerometer Sensors for Seismocardiography-Based Wearable Pre-Ejection Period Estimation
This study shows that combining gyroscope and accelerometer data improves heart health monitoring, making wearable devices more accurate for tracking cardiac function.
Recent Advances in Seismocardiography
This study reviews how SCG, a method to measure heart vibrations, is improving with new sensors and AI, showing promise for diagnosing heart conditions like atrial fibrillation and heart failure noninvasively.
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.
A Machine Learning Approach to Assess the Separation of Seismocardiographic Signals by Respiration
This study shows that machine learning can classify heart vibrations based on breathing patterns, with lung volume proving to be a better grouping method than respiratory phases for reducing signal variability.