Cardiac Time Intervals
Studies in this Category
Seismocardiography Pig Hypovolemia Dataset for Signal Quality Indexing and Validated Cardiac Timings
This study provides a high-quality dataset of heart vibrations from pigs, helping researchers develop better tools for tracking heart health using wearable sensors.
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.
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.
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.
Echocardiography Correlation with Seismocardiography—Systematic Review
This review highlights how SCG and ECHO can work together to improve heart monitoring, but calls for better standardization to make studies more reliable and comparable.
Developing a Protocol for Aligning and Correlating Seismocardiography with Echocardiography
This study developed a reliable ultrasound protocol to match heart vibration signals with cardiac events, improving research consistency and paving the way for better heart monitoring technologies.
Deep learning-based beat-to-beat delineation of heart sounds and fiducial points in seismocardiography
This study developed an AI tool that accurately detects key heart vibration points, enabling better heart monitoring for patients with or without heart disease.
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.
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.
Seismocardiography and echocardiography: the correlation in the systolic complex
This study shows that chest vibrations (SCG) can detect heart function changes and correlate with ultrasound results, offering a simpler way to monitor heart health at home or in clinics.
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.
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.
Porcine Model for Validation of Noninvasive Estimation of Pulmonary Hypertension
This study shows that chest vibrations measured by SCG can detect pulmonary hypertension in pigs, suggesting it could be a simpler, cheaper alternative to echocardiography for humans in the future.
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.
Deep Learning for identifying systolic complexes in SCG traces: a cross-dataset analysis
This study shows how deep learning can identify heart activity from chest vibrations, even in real-world conditions, by using data from multiple sensors and personalizing the model for each user.
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.
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.
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.
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.
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.
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.
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.
Can Seismocardiogram Fiducial Points Be Used for the Routine Estimation of Cardiac Time Intervals in Cardiac Patients?
This research shows that SCG signals can help monitor heart function but may not work for all patients. A preliminary test is needed to ensure accuracy before using this method in clinical settings.
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.
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.
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.
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.
A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications
This study reviews how SCG, a method to measure heart vibrations, is advancing with new sensors and AI to monitor heart health more effectively, even at home. It also highlights challenges like reducing noise in signals during movement.
Seismocardiography: Interpretation and Clinical Application
This research shows how heart vibrations (SCG) can help monitor heart health. It links SCG signals to heart events, tracks therapy effects in heart failure patients, and estimates fitness levels without exercise tests, making heart monitoring simpler and more accessible.
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.
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.
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.
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.
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.
Comparison of Different Methods for Estimating Cardiac Timings: A Comprehensive Multimodal Echocardiography Investigation
This study shows that chest vibrations (SCG) can measure heart function more accurately than traditional methods, paving the way for wearable heart monitors.
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.
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.
High-Resolution Seismocardiogram Acquisition and Analysis System.
This study developed a portable device that uses vibrations from the chest to monitor heart health, showing promising results in detecting heart function metrics similar to hospital-grade echocardiograms.
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.
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.
Contactless Mapping of Thoracic and Abdominal Movements: Applications for Seismocardiography
This study shows that a new ultrasound device can measure heart vibrations without touching the body, offering a faster and less invasive alternative to traditional methods.
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.
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.
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.
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.
Autonomic function testing aboard the ISS using “PNEUMOCARD”
This study shows that where SCG sensors are placed on the chest affects the accuracy of heart function measurements, highlighting the need for consistent placement standards to improve heart disease diagnosis.