Artifact Removal
Studies in this Category
Severe aortic stenosis detection using seismocardiography
This study shows that chest vibrations measured by a small device can accurately detect severe heart valve disease, offering a low-cost alternative to traditional tests like echocardiography.
SCG With Your Phone: Diagnosis of Rhythmic Spectrum Disorders in Field Conditions
This study shows that smartphones can reliably monitor heart rhythms using vibrations from the chest, thanks to advanced AI that works even in noisy, real-world conditions.
Fully automated template matching method for ECG-free heartbeat detection in cardiomechanical signals of healthy and pathological subjects
This study developed a new method to detect heartbeats from chest vibrations without needing ECG, showing high accuracy even for patients with heart diseases. It could enable long-term heart monitoring using wearable devices.
MSCardio Seismocardiography (SCG) Dataset
This dataset shows how smartphones can record heart vibrations to help researchers study heart health remotely and affordably.
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.
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.
Design of synchronous seismocardiography-ballistocardiography monitoring system
Researchers developed a wearable device that captures heart vibrations from the chest and body simultaneously, showing promise for use in clinics and at home to monitor heart health effectively.
Robustness of Persistence Diagrams to Time-Delay for Seismocardiogram Signal Quality Assessment*
This study shows that a new method using persistence diagrams can assess heart vibration signal quality without needing ECG, making it more reliable for wearable heart monitors in noisy environments.
Assessing the Effectiveness of Various Filtering Techniques on Seismocardiography Signals in Individuals with Valvular Heart Disease
This study tested different methods to clean heart vibration signals for better diagnosis of valve diseases, finding ICA to be the most effective at reducing noise while keeping the signal intact.
Seismocardiograph Monitoring Using SMS Fiber Structure with PDMS Enclosure
This study developed a fiber-optic heart monitoring system that is highly accurate and protected by a special material, making it more reliable and practical for detecting heart vibrations.
Accuracy of the Instantaneous Breathing and Heart Rates Estimated by Smartphone Inertial Units
This study shows that smartphones can accurately measure heart and breathing rates using built-in sensors, offering a simple and affordable way to monitor health without extra devices.
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.
Contactless seismocardiography via Gunnar-Farneback optical flow
This research shows that smartphone videos can track heart vibrations as accurately as traditional sensors, offering a comfortable and contactless way to monitor heart health.
A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography
This study developed a method to clean heart vibration signals for wearable devices, making them more accurate even during walking, without needing extra sensors like ECG. This could improve heart monitoring in daily life and hospitals.
Extracting Cardiovascular-Induced Chest Vibrations from Ordinary Chest Videos: A Comparative Study
This study shows that smartphone videos can accurately track heart vibrations using advanced computer vision methods, offering a comfortable and non-invasive way to monitor heart health.
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.
Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies
This study reviews wearable and remote devices for tracking breathing, from chest belts to advanced sensors like fiber optics and radar. These technologies could help monitor respiratory health at home or in clinics, improving care for conditions like asthma and sleep apnea.
Smartphone-Derived Seismocardiography: Robust Approach for Accurate Cardiac Energy Assessment in Patients with Various Cardiovascular Conditions
This study shows that smartphones can reliably measure heart vibrations to assess cardiac energy, making it easier for patients to monitor their heart health at home.
Postural and longitudinal variability in seismocardiographic signals
This study shows that SCG signals, which measure heart vibrations, change with posture but remain stable over time, making them promising for long-term heart monitoring.
Cross-Domain Detection of Pulmonary Hypertension in Human and Porcine Heart Sounds
This study shows that heart sound recordings from pigs can help train AI models to detect pulmonary hypertension in humans, offering a non-invasive and accurate alternative to invasive procedures like catheterization.
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.
Synthetic Seismocardiography Signal Generation by a Generative Adversarial Network
Researchers used AI to create realistic heart vibration signals, helping scientists train heart-monitoring systems without needing expensive patient data collection.
Revolutionizing smartphone gyrocardiography for heart rate monitoring: overcoming clinical validation hurdles
This study highlights how smartphone gyroscopes can accurately monitor heart rate, offering a practical and non-invasive alternative to traditional methods like ECG and PPG, even during daily activities.
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.
ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching
This study shows that heartbeats can be accurately detected from chest vibrations without needing an ECG, using a simple and efficient algorithm. This could enable wearable devices to monitor heart health more easily.
End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography
This study shows that smartphones can detect atrial fibrillation (AFib) using vibrations from the chest with high accuracy, offering a practical and affordable heart monitoring solution.
Toward Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals
This research shows how a wearable chest patch can estimate lung air volume using heart signals and machine learning, offering a step toward easy, continuous respiratory health monitoring.
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.
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.
Beat to BEAT – Non-Invasive Investigation of Cardiac Function on the International Space Station
This study tests a smart shirt that monitors astronauts' heart health in space, aiming to improve wearable health technology for both space and earth use.
Seismocardiography with Smartphones: No Leap from Bench to Bedside (Yet)
This study shows that while smartphones can measure heart vibrations, the technology isn’t ready for clinical use due to lack of validation and standardization compared to other methods like PPG.
A Comparison of Heart Pulsations Provided by Forcecardiography and Double Integration of Seismocardiogram
This study shows that heart vibrations measured by accelerometers can mimic a novel sensor's output, but improvements are needed for accurate heart rate tracking during breathing and apnea.
A multi-point heart rate monitoring using a soft wearable system based on fiber optic technology
This study developed a wearable device that uses advanced fiber optics to monitor heart rate more accurately by measuring chest vibrations. It could help doctors track heart health in clinical and daily settings.
Heart Rate and Respiratory Rate Monitoring Using Seismocardiography
This study shows that SCG can accurately measure heart and breathing rates, offering a non-invasive alternative to traditional methods like ECG and respiratory belts.
Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study (Preprint)
This study shows that a wearable device can accurately measure blood pressure without a cuff, helping underserved communities monitor hypertension remotely and conveniently.
The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care
This study reviews how BCG and SCG technologies are being revived to monitor heart and health conditions, with potential applications in sleep and cardiovascular care. It calls for making these technologies more accessible and standardized for everyday use.
Smart Seismocardiography: A Machine Learning Approach for Automatic Data Processing
This research shows how a low-cost sensor and machine learning can track heart vibrations to monitor cardiac health, paving the way for affordable wearable devices.
Motion artifact cancellation from a single channel SCG using adaptive forgetting factor recursive least square filter
This study developed a new method to clean heart vibration signals from motion noise, achieving near-perfect accuracy compared to ECG readings, even during activities like jogging and jumping.
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.
High-Accuracy, Unsupervised Annotation of Seismocardiogram Traces for Heart Rate Monitoring
This study shows how chest vibrations can be used to monitor heartbeats accurately without needing traditional ECG sensors, paving the way for wearable heart monitors in daily life.
Influence of sympathetic activation on myocardial contractility measured with ballistocardiography and seismocardiography during sustained end-expiratory apnea
This study shows that heart vibrations measured during breath-holding can reveal changes in nerve activity linked to sleep disorders, offering a new way to monitor heart health non-invasively.
Seismocardiography on Infants and Kids
This research shows how SCG can track heart activity in infants and kids, revealing unique signal patterns compared to adults. It sets the stage for better heart monitoring tools for children.
Trodden Lanes or New Paths: Ballisto- and Seismocardiography Till Now
This study reviews research on heart vibration methods (BCG and SCG) and finds growing interest due to better sensors and technology, paving the way for improved heart diagnostics.
Quantification of cardiac kinetic energy and its changes during transmural myocardial ischemia assessed by multi-dimensional seismocardiography
This research shows how a vibration-based heart monitoring system can track heart damage during a heart attack and recovery, offering a new way to measure heart function remotely and non-invasively.
A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography
This study developed a new method to clean heart vibration signals from motion noise, achieving 98% accuracy in detecting heartbeats during walking and standing, using a single wearable sensor.
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.
Heart Beat Detection from Smartphone SCG Signals: Comparison with Previous Study on HR Estimation
This study shows that smartphones can accurately detect heartbeats using vibrations from the chest, with improved algorithms achieving near-perfect accuracy.
Heart Rate Variability Analysis on Reference Heart Beats and Detected Heart Beats of Smartphone Seismocardiograms
This study shows that smartphones can accurately measure heart rate variability using chest vibrations, paving the way for affordable heart monitoring at home.
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.
A seismocardiography system and a possibility of its use for diagnosis of internal organs diseases using seismocardiogram information analysis
This research shows how heart vibrations measured by a new device can help diagnose internal organ diseases, offering a simpler alternative to traditional heart monitoring methods like ECGs.
A Low-Cost System for Seismocardiography-Based Cardiac Triggering: A Practical Solution for Cardiovascular Magnetic Resonance Imaging at 3 Tesla
This study shows that a new low-cost heart monitoring system using vibrations (SCG) works as well as traditional ECG systems during MRI scans, while being easier to use and more reliable in high magnetic fields.
Design and Development of a Portable Recording System for Simultaneous Acquisition of SCG and ECG Signals
This research developed a portable device that uses vibrations from the chest to monitor heart and breathing activity, showing promise for easier heart health tracking alongside traditional ECG tests.
A Unified Framework for Quality Indexing and Classification of Seismocardiogram Signals
This study shows how a new method can improve the quality and analysis of heart vibration signals, helping detect issues like misplaced sensors with high accuracy. It could make heart monitoring more reliable and automated for patients and clinicians.
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.
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.
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.
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.
A Hidden Markov Model for Seismocardiography
This study shows that heart vibrations can be analyzed using a mathematical model to measure heart rate and other metrics with high accuracy, even using inexpensive sensors at home.
BCG Artifact Removal Using Improved Independent Component Analysis Approach
This research presents a new method to clean heart vibration signals (BCG) by removing noise caused by movement, using advanced mathematical techniques like ICA and clustering. It improves signal quality for better health monitoring.
Automatic Identification of Systolic Time Intervals in Seismocardiogram
This research shows how wearable sensors can accurately track heart function by analyzing vibrations from the chest, even in noisy conditions, paving the way for continuous heart health monitoring.
Heart Rate Variability Estimation with Joint Accelerometer and Gyroscope Sensing
This study shows how combining accelerometer and gyroscope sensors can improve heart rate variability tracking, paving the way for better wearable heart monitors.
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.
A new algorithm for segmentation of cardiac quiescent phases and cardiac time intervals using seismocardiography
This study shows how chest vibrations can measure heart mechanics and identify resting phases of the heart, which could improve imaging and early disease detection without expensive equipment.
Application of Acceleration Sensors in Physiological Experiments
This study shows how accelerometers can monitor heart activity and breathing, paving the way for wearable health devices that track fitness and medical conditions more effectively.
Beat-to-beat estimation of LVET and QS2 indices of cardiac mechanics from wearable seismocardiography in ambulant subjects
This study shows that smartphones can accurately detect heartbeats using vibrations from the chest, with improved algorithms achieving near-perfect accuracy.
Combined measurement of ECG, Breathing and Seismocardiograms DataBase (CEBSDB)
This dataset combines heart, breathing, and vibration signals to study how breathing affects heart rate measurements and improve vibration-based heart monitoring technologies.
Wearable Seismocardiography
This study shows that wearable devices can use heart vibrations and AI to diagnose aortic valve problems and predict blood flow metrics as accurately as advanced MRI scans, offering a cheaper and faster alternative for heart health monitoring.
Seismocardiographic changes associated with obstruction of coronary blood flow during balloon angioplasty
This study shows that seismocardiography can detect heart muscle changes during coronary angioplasty, offering a new way to monitor heart health noninvasively.
Seismocardiography: waveform identification and noise analysis
The study examines how to clean and classify heart vibration signals (SCG) for better medical use, focusing on reducing noise and improving accuracy.