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Deep Learning Predicts Cardiac Output from Seismocardiographic Signals in Heart Failure
This study shows that wearable sensors using chest vibrations and heart signals can estimate heart function as accurately as invasive tests, offering a safer and more accessible option for heart failure patients.
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.
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.
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.
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.
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.
Driver Cardiovascular Disease Detection Using Seismocardiogram
This research shows how vibrations from the heart, measured through a car's safety belt, can monitor drivers' heart health and prevent accidents caused by sudden heart issues.
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.
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.
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.
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.
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.
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.
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.
Ballistocardiography and Seismocardiography: A Review of Recent Advances
This paper reviews how new technologies like wearable sensors and advanced signal processing make heart monitoring through vibrations (BCG and SCG) more practical and clinically useful, even outside hospitals.
A System for Seismocardiography-Based Identification of Quiescent Heart Phases: Implications for Cardiac Imaging
This study shows that SCG, a chest vibration signal, can better identify heart motion phases for clearer CT scans, potentially reducing radiation and improving heart disease diagnosis.
The seismocardiogram as magnetic-field-compatible alternative to the electrocardiogram for cardiac stress monitoring
This study shows that SCG can monitor heart function during MRI without interference, offering a safer and more reliable way to detect heart issues like ischemia compared to ECG.