Dynamic Time Warping
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.
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.
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.
A deep learning approach to using wearable seismocardiography (SCG) for diagnosing aortic valve stenosis and predicting aortic hemodynamics obtained by 4D flow MRI
This study shows that wearable heart vibration sensors combined with AI can predict blood flow and diagnose aortic valve problems as accurately as advanced MRI scans, offering a cheaper and faster alternative for heart disease screening.
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.
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.
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.
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.
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.
Identifying Patients With Coronary Artery Disease Using Rest and Exercise Seismocardiography
This study shows that heart vibrations measured during rest and exercise can detect coronary artery disease as accurately as advanced imaging tests, offering a cheaper and faster alternative for diagnosis.
Wearable ballistocardiogram and seismocardiogram systems for health and performance
This study shows how wearable sensors can track heart health by measuring vibrations caused by heartbeats, offering a low-cost way to monitor conditions like heart failure and optimize physical performance in challenging environments.
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.
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.