The Science of Mechanical Cardiac Insights
OpenSCG.org provides a transparent, peer-reviewed foundation for smartphone-based Seismocardiography, powered by a semantic Knowledge Graph.
Trending Topics
Clinical Benchmarks
Evidence-based Answer Machine v2.0
| Application | Validated Results | Top Evidence | Confidence |
|---|---|---|---|
Dataset | 17,000+ Annotated Beats | View Analysis | Cornerstone |
Clinical Validation | HF Detection Feasibility | View Analysis | Cornerstone |
Signal Processing | ECG-Free Accuracy | View Analysis | Cornerstone |
AI/ML | ROC-AUC up to 0.95 | View Analysis | Cornerstone |
Evidence-Based FAQ
What is the primary contribution of this dataset?
The dataset provides rigorously annotated SCG signals with validated fiducial points and signal quality indices, addressing the lack of standardized SCG datasets.(seismocardiography-pig-hypovolemia-dataset-for-signal-quality-indexing-and-validated-cardiac-timings)
How were fiducial points validated?
Fiducial points were validated against gold-standard catheter-based measurements of aortic opening and closing events.(seismocardiography-pig-hypovolemia-dataset-for-signal-quality-indexing-and-validated-cardiac-timings)
What was the diagnostic accuracy of the SCG-based algorithm?
The algorithm achieved a sensitivity of 92%, specificity of 87.8%, and an area under the curve of 96%.(severe-aortic-stenosis-detection-using-seismocardiography)
What are the potential clinical applications of this technology?
The technology could be used for population-wide screening or detecting appropriate patients for echocardiography referral in asymptomatic subjects presenting with a systolic murmur.(severe-aortic-stenosis-detection-using-seismocardiography)
What physiological changes were observed in cardiac feature points post-exercise?
Post-exercise, the AC and MO feature points shifted earlier across all channels, reflecting shortened systolic and diastolic durations.(multi-site-cardiac-rhythm-monitoring-via-multi-channel-scg-system-and-exercise-induced-physiological-analysis)
Research Domains
Semantic clusters derived from the Seismocardiography Knowledge Graph.
signal-processing
EXPLORE ALL 78 STUDIESFully automated template matching method for ECG-free heartbeat detection in cardiomechanical signals of healthy and pathological subjects
Salvatore Parlato, Jessica Centracchio, Daniele Esposito, Paolo Bifulco, Emilio Andreozzi
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.
Estimation of cardiorespiratory fitness in healthy using seismocardiography
Samuel Emil Schmidt, M. T. Hansen, Dan Stieper Karbing, Kasper Sørensen, Mathias Krogh Poulsen, Tue Rømer, Johannes J. Struijk, Peter Søgaard, Jørn Wulff Helge
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.
Digital Twin-Based Investigation of Seismocardiogram Sensitivity to Tissue Mechanics and Myocardial Motion
Mohammadali Monfared, Bahram Kakavand, Peshala Thibbotuwawa Gamage, Amirtahà Taebi
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.
validation
EXPLORE ALL 53 STUDIESSevere aortic stenosis detection using seismocardiography
Jouni Pykäri, Ismail Elnaggar, Antti Airola, Antti Airola, Tero Koivisto, Tuija Tammelin och Tommi Vasankari, Mikko Savontaus
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.
SEISMIC-HF 1: key findings from AHA24 and implications for remote cardiac monitoring
Baljash Cheema, Anjan Tibrewala
This study shows that a wearable patch can estimate heart pressure in patients with heart failure as accurately as invasive tests, offering hope for better remote care options.
Developing a Protocol for Aligning and Correlating Seismocardiography with Echocardiography
Dennis Lawin, Ulf Kulau, Urs‐Vito Albrecht
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.
ai-ml
EXPLORE ALL 11 STUDIESSCG With Your Phone: Diagnosis of Rhythmic Spectrum Disorders in Field Conditions
Peter Golenderov, Yaroslav Matushenko, A. Tushina, Michal Barodkin
This study shows that smartphones can reliably monitor heart rhythms using vibrations from the chest, thanks to advanced AI that works even in noisy, real-world conditions.
Deep Learning Predicts Cardiac Output from Seismocardiographic Signals in Heart Failure
Jesse Wang, Mehdi Nouraie, Neil J. Kelly, Stephen Y. Chan
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.
Deep learning-based beat-to-beat delineation of heart sounds and fiducial points in seismocardiography
Emil Korsgaard, Ahmad Agam, Peter Søgaard, Kasper Emerek, Kasper Sørensen, Jørn Wulff Helge, Johannes J. Struijk, Samuel Emil Schmidt
This study developed an AI tool that accurately detects key heart vibration points, enabling better heart monitoring for patients with or without heart disease.
dataset
EXPLORE ALL 5 STUDIESSeismocardiography Pig Hypovolemia Dataset for Signal Quality Indexing and Validated Cardiac Timings
Michael J. Cho, Cem Okan Yaldiz, Afra Nawar, Vikram Abbaraju, Ryan C. Emadi, Onur S. Kilic, Zeineb Bouzid, Farhan N. Rahman, Chuoqi Chen, Jacob M. Cook, Ahmet Rasim Emirdagi, Roshan L. Saigal, Mickey Paulus, Omer T. Inan
This study provides a high-quality dataset of heart vibrations from pigs, helping researchers develop better tools for tracking heart health using wearable sensors.
MSCardio Seismocardiography (SCG) Dataset
Amirtahà Taebi, Mohammad Muntasir Rahman
This dataset shows how smartphones can record heart vibrations to help researchers study heart health remotely and affordably.
A Forcecardiography dataset with simultaneous SCG, Heart Sounds, ECG, and Respiratory signals
Salvatore Parlato, Jessica Centracchio, Eliana Cinotti, Maria Virginia Manzi, Grazia Canciello, Maria Prastaro, Maria Lembo, Benjamin M. Brandwood, Gaetano D. Gargiulo, Paolo Bifulco, Giovanni Esposito, Raffaele Izzo, Emilio Andreozzi
This study provides a groundbreaking dataset combining heart and breathing signals, enabling researchers to improve non-invasive heart and lung monitoring technologies.
hardware
EXPLORE ALL 7 STUDIESA trimodal system for the acquisition of synchronous echocardiography, electrocardiography, and seismocardiography data
Carson A. Wick, Jin-Jyh Su, Oliver Brand, James H. McClellan, Pamela Bhatti, Srini Tridandapani
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.
Design of synchronous seismocardiography-ballistocardiography monitoring system
Xinyu Liu, Jiangtao Li
Researchers developed a wearable device that captures heart vibrations from the chest and body simultaneously, showing promise for use in clinics and at home to monitor heart health effectively.
Seismocardiograph Monitoring Using SMS Fiber Structure with PDMS Enclosure
Frans Rizal Agustiyanto, Agus Muhamad Hatta, Dhany Arifianto, Mahenda Radityo, Maulana Santoso, Selfi Stendafity, Budi Susetyo Pikir
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.
review
EXPLORE ALL 12 STUDIESEchocardiography Correlation with Seismocardiography—Systematic Review
Dennis Lawin, Ulf Kulau, Urs‐Vito Albrecht
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 Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications
Deepak Rai, Hiren Kumar Thakkar, Shyam Singh Rajput, José Santamaría, Chintan Bhatt, Francisco Roca
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.
Trodden Lanes or New Paths: Ballisto- and Seismocardiography Till Now
Nico Jähne-Raden, Henrike Gütschleg, Michael Marschollek
This study reviews research on heart vibration methods (BCG and SCG) and finds growing interest due to better sensors and technology, paving the way for improved heart diagnostics.
Open Research & Collaboration
OpenSCG.org is an open-source initiative. We welcome collaboration with academic institutions, clinical research organizations, and independent developers.