Heart Rate Variability
Studies in this Category
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.
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.
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.
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.
Publicly available signal databases containing seismocardiographic signals — the state in early 2023
This study highlights the limited availability of SCG signal databases, which are crucial for advancing heart monitoring research. It identifies gaps in gender balance and disease representation in existing datasets.
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.
Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases
This study shows that heart vibrations measured by chest sensors (SCG and GCG) can reliably track heart rate variability, even in patients with heart valve diseases, offering a simpler alternative to traditional ECG tests.
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.
Heartbeat Detection in Seismocardiograms with Semantic Segmentation
This study shows that a deep learning model can accurately detect heartbeats from chest vibrations, offering a promising alternative to traditional ECG-based methods for heart monitoring.
BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting Heart Activity
This research shows how wearable sensors can use heart vibrations to identify people with high accuracy, even weeks after enrollment. It also demonstrates how these sensors can recognize activities like walking or lying down, making them useful for secure health monitoring.
Comparison of Heart Rate Variability Indices Based on Seismocardiograms from Healthy Volunteers and Patients with Valvular Heart Diseases
This research shows that heart vibrations measured from the chest can help detect differences in heart rate patterns between healthy people and those with heart valve diseases, offering a new way to monitor heart health outside clinics.
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.
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.
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.
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.
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.
Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms on Healthy Volunteers
This study shows that heart vibrations measured with simple sensors can reliably track heart rate variability, even in patients with heart valve diseases, making heart monitoring more accessible and affordable.
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 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.
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.
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.
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.
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.
History of space medicine: Academician Vasily V. Parin, founder of space cardiology
This study explores how Vasily V. Parin's innovations in heart monitoring during space missions have shaped modern cardiology and space medicine, including tools now used in everyday healthcare.
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.
A trimodal system for the acquisition of synchronous echocardiography, electrocardiography, and seismocardiography data
This study developed a system that combines ultrasound, heart electrical signals, and chest vibrations to better understand heart function and improve imaging techniques like CT and MRI.
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.