Signal Processing
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.
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.
Monitoring of respiration and cardiorespiratory interactions from multichannel seismocardiography signals
This study shows that chest vibrations measured by accelerometers can accurately track breathing and heart-lung interactions, regardless of sensor placement. It introduces a new method to analyze these signals for better health monitoring.
From video to vital signs: a new method for contactless multichannel seismocardiography
This study shows that smartphone videos can track heart vibrations using QR stickers on the chest, offering a low-cost way to monitor heart health and detect issues early, with accuracy comparable to clinical tools.
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.
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.
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.
LubDubDecoder: Bringing Micro-Mechanical Cardiac Monitoring to Hearables
This study shows how regular earbuds can monitor heart health by detecting subtle vibrations linked to heartbeats, offering a convenient way to track cardiovascular health daily.
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.
Noncontact Multipoint Vital Sign Monitoring With mmWave MIMO Radar
This study shows how radar technology can monitor heart and lung movements at multiple chest points without physical contact, offering accurate and comfortable health tracking compared to traditional methods.
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.
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.
ECG-Free Assessment of Cardiac Valve Events Using Seismocardiography
This study shows that heart valve events can be detected using body vibrations alone, without the need for ECG, making heart monitoring simpler and more accessible.
Seismocardiography for Emotion Recognition: A Study on EmoWear with Insights from DEAP
This study shows that a single wearable accelerometer on the chest can track emotions by measuring heart and breathing vibrations, offering a simpler and cheaper way to integrate emotion recognition into daily life.
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.
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.
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.
Waveform Similarity Analysis Using Graph Mining for the Optimization of Sensor Positioning in Wearable Seismocardiography
This study shows that placing a wearable heart sensor near the mitral valve while lying down gives the most consistent readings, helping improve heart monitoring accuracy for future clinical use.
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.
Analysis of Non-Contact Multichannel Recording of Cardiac Vibration: Visual Seismocardiogram
This study uses ultrasound to record heart vibrations without touching the body, offering better accuracy and visualization for heart event detection compared to traditional methods.
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.
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.
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.
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.
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.
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.
Computer-Aided Detection of Fiducial Points in Seismocardiography through Dynamic Time Warping
This study shows how advanced algorithms can improve heart monitoring by accurately detecting key heart signals from chest vibrations, helping predict heart failure with over 92% accuracy.
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.
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.
Effect of Normal Breathing and Breath Holding on Seismocardiographic Signals and Heart Rate
This study shows that holding your breath can make heart vibration signals more consistent, which could help improve heart health monitoring techniques.
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.
Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring
This study shows how chest vibrations measured by accelerometers can detect heart activity using advanced wavelet algorithms, even without ECG. The methods work well in resting conditions but need improvement for noisy environments like breathing tasks.
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.
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.
Multichannel seismocardiography: an imaging modality for investigating heart vibrations
Researchers developed a new system to map heart vibrations across the chest, revealing patterns tied to heart valve movements. This technology could improve heart failure treatments and diagnostics.
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.
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.
Contactless Seismocardiography via Deep Learning Radars
This research shows how radar and AI can monitor heart vibrations without physical contact, achieving accuracy similar to clinical ultrasound for detecting key heart movements.
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.
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.
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.
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.
Near Real-Time Implementation of An Adaptive Seismocardiography – ECG Multimodal Framework for Cardiac Gating
This research shows that combining heart vibration signals (SCG) with ECG improves the accuracy of heart imaging, making it safer and more effective for diagnosing heart diseases.
Visualization of the Multichannel Seismocardiogram
This study explores ways to visualize chest vibrations caused by heart activity using data from 16 sensors. The methods help researchers better understand how these vibrations relate to heart function.
Influence of Gravitational Offset Removal on Heart Beat Detection Performance from Android Smartphone Seismocardiograms
This study shows that smartphones can accurately detect heartbeats using vibrations from the chest, even without removing gravitational effects, thanks to advanced signal processing techniques.
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.
Definition of Fiducial Points in the Normal Seismocardiogram
This research shows how chest vibrations (SCG) can accurately track heart valve movements, offering a simple, non-invasive way to monitor heart health using accelerometers.
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.
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.
Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health
This research shows how wearable chest sensors can measure heart function during walking by reducing motion noise, potentially helping doctors monitor heart health during daily activities.
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.
Challenges in Using Seismocardiography for Blood Pressure Monitoring
This study explored using heart vibrations and pulse signals to estimate blood pressure but found inconsistent results, showing the method needs improvement before clinical use.
A Cardiac Early Warning System with Multi Channel SCG and ECG Monitoring for Mobile Health
This study shows how wearable sensors can monitor heart health by combining electrical and mechanical heart signals, offering an affordable and reliable early warning system for heart disease.
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 3D model of the thorax for seismocardiography
This study creates a 3D model of the chest to better understand heart vibrations, helping doctors use SCG for heart 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.
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.
Three-dimensional apex-seismocardiography
This study used a 3D accelerometer to measure heart vibrations at the chest's apex, revealing complex movement patterns that could help in diagnosing heart conditions like heart failure in the future.
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.
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.
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.
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.
Comparative analysis of three different modalities for characterization of the seismocardiogram
This study explores three methods to analyze heart vibrations, showing how imaging and modeling can help understand heart mechanics and improve non-invasive diagnostics.
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.