Regression
Studies in this Category
SEISMIC-HF 1: key findings from AHA24 and implications for remote cardiac monitoring
This study shows that a wearable patch can estimate heart pressure in patients with heart failure as accurately as invasive tests, offering hope for better remote care options.
A Forcecardiography dataset with simultaneous SCG, Heart Sounds, ECG, and Respiratory signals
This study provides a groundbreaking dataset combining heart and breathing signals, enabling researchers to improve non-invasive heart and lung monitoring technologies.
Estimation of cardiorespiratory fitness in healthy using seismocardiography
This study shows that a chest vibration sensor can accurately measure fitness levels without exercise, offering a simple and affordable way to track heart health.
Seismocardiography-based estimation of hemodynamic parameters during submaximal ergometer test
This study shows that a wearable chest sensor can estimate heart function during exercise recovery, but struggles with accuracy during active cycling due to motion. It highlights the potential for simple, non-invasive heart monitoring in low-motion settings.
Non-exercise estimation of peak oxygen uptake in patients with ischaemic heart disease and heart failure using seismocardiography
This study found that a new heart monitoring method using vibrations (SCG) was not accurate enough to estimate fitness levels or track improvements in heart patients after rehabilitation.
Seismocardiography as a valuable non-exercise method for estimating peak Vo2 in cardiac patients? first experiences in Germany
This research shows that SCG can estimate heart fitness in cardiac patients almost as accurately as traditional exercise tests, but more data is needed to improve reliability for clinical use.
Evaluating Seismocardiography as a Non-Exercise Method for Estimating Maximal Oxygen Uptake
This study shows that the Seismofit® device can estimate fitness levels (VO2MAX) without exercise, offering a simpler alternative to traditional lab tests with good accuracy and reliability.
Point-of-care aid-to-diagnosis for heart failure using artificial intelligence based on seismocardiography acquired with a smartphone in the emergency department
This study shows that a smartphone app using heart vibrations and AI can help diagnose heart failure quickly and accurately in emergency settings.
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.
Accuracy of a Clinical Applicable Method for Prediction of VO2max Using Seismocardiography
This study shows that a chest vibration-based method (SCG) can accurately predict fitness levels (VO2max) in healthy adults, potentially offering a simpler alternative to traditional exercise tests.
Respiratory Modulation of Sternal Motion in the Context of Seismocardiography
This study shows how chest vibrations (SCG) can track breathing and heart activity using a single wearable sensor, paving the way for simpler health monitoring devices.
Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients With Heart Failure: A Feasibility Study
This study shows that a wearable patch can track heart pressure changes in heart failure patients, offering a cheaper way to monitor their condition remotely and reduce hospital visits.
Wearable Seismocardiography‐Based Assessment of Stroke Volume in Congenital Heart Disease
This study shows that a wearable device using chest vibrations and heart signals can estimate blood flow in children with heart defects, offering a way to monitor heart health remotely and affordably.
86057 High Screening Efficacy Using Wearable Seismocardiography to Identify Aortic Valve Disease Patients, Potential to Tailor MRI Exams to Patient Needs
This research shows that chest vibration signals can accurately identify heart valve disease, offering a quick and affordable alternative to MRI for screening patients.
Assessment of left ventricular twist by 3D ballistocardiography and seismocardiography compared with 2D STI echocardiography in a context of enhanced inotropism in healthy subjects
This research shows that vibrations from the heart, measured using wearable sensors, can predict heart function and twisting motion more accurately than traditional methods, offering a new way to monitor heart health remotely.
Validity and reliability of a clinical non-exercise method for assessment of cardiorespiratory fitness using seismocardiography
This study shows that seismocardiography can reliably measure cardiorespiratory fitness without exercise, though it slightly underestimates results compared to traditional methods.
Determination of Maximal Oxygen Uptake Using Seismocardiography at Rest
This study explores using chest vibrations (SCG) to estimate fitness levels without exercise. While the method shows potential, it needs refinement to match clinical accuracy standards.
Non-Invasive Wearable Patch Utilizing Seismocardiography for Peri-Operative Use in Surgical Patients
This study shows that a wearable patch can accurately monitor heart function during and after surgery, offering a non-invasive alternative to traditional methods.
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.
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.
Automatic Detection of Seismocardiogram Sensor Misplacement for Robust Pre-Ejection Period Estimation in Unsupervised Settings
This research shows that SCG sensors must be correctly placed on the chest to measure heart function accurately. A machine learning algorithm helps users detect misplacement, improving home-based heart monitoring for heart failure patients.
Universal Pre-Ejection Period Estimation Using Seismocardiography: Quantifying the Effects of Sensor Placement and Regression Algorithms
This study shows that placing heart vibration sensors below the clavicle improves heart function tracking accuracy, and wearable devices can work over thin clothing without losing precision.
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.