The Science of Mechanical Cardiac Insights
OpenSCG.org provides a transparent, peer-reviewed foundation for smartphone-based Seismocardiography, backed by over 30 years of clinical research.
Clinical Performance
Validated against Echo, MRI, and Angiography
Scientific Syntheses
Deep-dive analyses grouping decades of evidence into clear, actionable knowledge modules.
The CAD Evolution
How stress SCG accurately predicts anatomic CAD and estimates PCWP in clinical settings.
Smartphone Sensors
Technical breakdown of MEMS accelerometer noise floors and advanced ML denoising.
Cardiac Cycle Blueprint
Technical reference for AO, AC, MO, and MC waveforms validated against echocardiography.
Research Clusters
Our bibliography is organized by all 5 clinical and technical domains to facilitate targeted academic review.
Gold-Standard Comparisons
7 STUDIESDehkordi 2019: Multimodal Echo Investigation
Parastoo Dehkordi et al.
Establishes a quantitative comparison between SCG and ICG, suggesting higher accuracy for SCG in estimating systolic time intervals against ultrasound references.
Sørensen 2018: Definition of Fiducial Points
Kasper Sørensen et al.
Serves as a reference for identifying SCG fiducial points based on synchronized ultrasound data in healthy subjects.
Crow 1994: Precision and Consistency
R. S. Crow et al.
Provides early validation of SCG as a tool for evaluating mechanical events in the cardiac cycle, showing consistency with ultrasound-based measures.
Işilay Zeybek 2022: SCG in Cardiac Patients
Işilay Zeybek, Marco Di Rienzo et al.
Identifies challenges in applying standard SCG analysis to patients with heart disease, noting signal distortion in 38% of the cohort.
Kolind 2024: SCG/Echo Systolic Correlation
Christoffer Mejling Kolind et al.
S012 is a very recent (2024) validation of SCG’s sensitivity to hemodynamic shifts. Crucially, it found that SCG was *more* sensitive to small changes in preload than traditional ultrasound, highlighting its potential for early detection of volume overload in heart failure patients at home.
Castiglioni 2022: SCG for Sleep Monitoring
Castiglioni et al.
Validates the use of mechanocardiography for long-term physiological monitoring during sleep.
Agam 2022: Diastolic SCG vs Echo
Ahmad Agam et al.
Validates SCG’s potential for evaluating diastolic function. Note: This source was previously referenced as S038 in some contexts.
CAD Detection & Ischemia
3 STUDIESDehkordi 2019: SCG for CAD Screening
Parastoo Dehkordi et al.
Examines the diagnostic performance of SCG in CAD screening, observing results comparable to stress-echocardiography in this cohort.
Salerno 1992: Exercise SCG for CAD detection
Salerno DM, Zanetti J. et al.
S017 is a landmark historical validation. It provides the empirical proof that mechanical heart vibrations are more sensitive indicators of reduced blood flow (ischemia) than electrical changes, justifying OpenSCG's focus on SCG for high-precision cardiac screening.
Wilson 1993: Diagnostic Accuracy of SCG for CAD
R. A. Wilson et al.
Historical multicenter study highlighting the incremental diagnostic value of mechanical heart signals over traditional stress testing.
Heart Failure & Congestion
7 STUDIESHaddad 2024: Smartphone HF Recognition
Mona Haddad et al.
Validates the use of built-in smartphone MEMS for identifying heart failure patients in both inpatient and ambulatory settings.
SEISMIC-HF 1 (AHA 2024): Congestion Monitoring
SEISMIC-HF Investigators et al.
Provides summary evidence from the large-scale SEISMIC-HF 1 trial for non-invasive filling pressure estimation.
Noda 2017: PEP/LVET in Heart Failure
Noda et al.
S013 provides a direct link between SCG metrics and advanced clinical interventions like CRT. It proves that the 'vibration-based' markers OpenSCG extracts have real diagnostic and therapeutic value in treating high-risk heart failure patients.
Inan 2018: Assessing Clinical Status of HF
Omer T. Inan et al.
S016 is a vital clinical validation for OpenSCG’s heart failure monitoring use case. By proving that SCG can track the clinical improvement from hospital admission to discharge, it establishes SCG as a sensitive 'digital biomarker' for hemodynamic congestion and recovery, potentially more sensitive than heart rate alone.
Ganti 2022: Stroke Volume in CHD
V. Ganti et al.
S034 is an important feasibility study that tests SCG in a complex patient population (CHD). By using Cardiac MRI as a comparator, it establishes a benchmark for non-invasive Stroke Volume monitoring in remote scenarios, while highlighting the benefit of multi-modal sensing.
Hansen 2024: SCG for Maximal Oxygen Uptake in Sport
Hansen et al.
S035 extends the utility of OpenSCG from medical screening to sports performance. By linking resting vibrations to VO2max, it proves that SCG is a multi-dimensional tool capable of monitoring both cardiac disease and physical fitness.
Ebrahimkhani 2023: SCG for Aortic Valve Stenosis
D. Ebrahimkhani et al.
Demonstrates the capability of SCG to capture high-fidelity hemodynamic information previously thought to require advanced imaging.
Technical Reports & MEMS
22 STUDIESIbrahim 2022: OS Sampling Jitter in Smartphones
Osborne et al.
S008 is a vital technical warning. While smartphones are powerful, their operating systems are not designed for medical precision. This study justifies why OpenSCG's backend does not just take 'raw numbers' but performs complex synchronization to ensure clinical-grade reliability.
Landreani 2016: Beat-to-beat HR by Smartphone
Federica Landreani et al.
S009 is an early but rigorous validation (EMBC 2016) of using commodity smartphones for SCG. It laid the groundwork for OpenSCG by proving that the internal sensors of a modern phone are sensitive enough to match the temporal precision of a professional ECG, provided the phone is positioned correctly.
Taebi 2019: Recent Advances in SCG
Amirtaha Taebi et al.
S014 is the definitive state-of-the-art review for SCG. For OpenSCG, it serves as an academic bridge that links our modern AI approach to over 60 years of cardiomechanical research, proving that the technology is now 'clinically ready' for ubiquitous deployment.
Cinotti 2025: Instantaneous HR via Phone
Cinotti et al.
Recent 2025 benchmark confirming smartphone hardware sensitivity for medical-grade cardiac and respiratory monitoring.
MODE-AF Study: Mobile Phone Detection of AF
Tero Koivisto et al.
Validates the effectiveness of combined accelerometer and gyroscope data for heart rhythm screening without external hardware.
MSCardio: Remote Monitoring Dataset
Amirtaha Taebi et al.
An open-access dataset used for training and validating algorithms on diverse smartphone hardware.
OpenSCG: Telemedicine Waveform Framework
OpenSCG Team et al.
Technical infrastructure supporting scalable data streaming for research purposes.
Ramos-Castro 2012: Smartphone HRV Challenges
Capdevila et al.
S021 is a foundational technical warning for the mobile health industry. For OpenSCG, it provides the historical and scientific justification for our sophisticated backend synchronization—proving that we solve a known industry problem that prevents most competitors from achieving clinical-grade accuracy.
Zia 2022: Quality Indexing and Classification
Jonathan Zia et al.
Provides the technical foundation for robust signal quality control in mobile SCG monitoring.
Lahdenoja 2018: AF Detection via Smartphone
Olli Lahdenoja et al.
Established the technical feasibility of smartphone-only arrhythmia screening.
Rahmani 2024: EmoWear Dataset
A. Rahmani et al.
S030 (EmoWear) is a pioneer in 'Everyday SCG'. While most studies focus on medical diagnosis, EmoWear proves that SCG can capture subtle physiological changes related to human emotions and daily actions. This is key for OpenSCG’s vision of a holistic 'Health & Well-being' monitor that understands the user’s context.
Inan 2014: Review of Recent Advances
Omer T. Inan et al.
S031 is the highly cited 'manifesto' of modern SCG research. For OpenSCG, it provides the authoritative background needed to justify why SCG technology is now ready for consumer and clinical scale after decades of dormancy.
Ha 2020: Contactless SCG via Radars
Ha et al.
Explores the feasibility of passive, contact-free cardiac monitoring using millimeter-wave technology.
Hossein 2024: Robust Cardiac Energy Assessment
Hossein et al.
Validates the repeatability of patient-performed SCG measurements in a real-world telemedicine context.
Shafiq 2016: Automatic STI Identification
Ghufran Shafiq et al.
S036 (Shafiq 2016) is a foundational paper for OpenSCG’s automated processing pipeline. By moving beyond lab-only supine measurements to seated trials and achieving millisecond-level precision, it validates the core algorithms needed for practical, non-invasive STI monitoring.
Centracchio 2023: ECG-Free Heartbeat Detection
J. Centracchio et al.
S037 is a significant validation for OpenSCG’s core algorithm. By testing on a large cohort of 77 *pathological* subjects (not just healthy ones), it confirms that SCG-based heart rate monitoring is robust against the signal distortions caused by valvular heart diseases.
Zia 2020: Resilient STI Estimation
Shafiq et al.
Pioneering work on motion-robust seismocardiography algorithms.
Sørensen 2020: SCG in Pregnancy
Sørensen et al.
Application of cardiomechanical sensing to maternal health and obstetrics.
García-González 2013: CEBS Database
M. A. García-González et al.
S041 (CEBS Database) is a 'gold standard' dataset for technical SCG research. For OpenSCG, it serves as the primary benchmarking tool for validating our signal processing filters and basic peak detection logic before moving to complex real-world data.
Postolache 2010: Bed-Integrated SCG
Leitão et al.
Early demonstration of 'smart environment' heart monitoring using mechanical sensors.
Parlato 2025: FOSTER Multimodal Dataset
A. Parlato et al.
S045 (FOSTER) is a high-quality 2025 dataset that represents the future of multimodal 'Force-based' monitoring. For OpenSCG, it provides the precise temporal alignment needed to train models that combine vibrations (SCG) with acoustic data (PCG), enabling more robust detection of heart valve events.
Golenderov 2026: Rhythmic Spectrum Disorders in Field
Golenderov et al.
Demonstrates the effectiveness of advanced neural networks in processing heterogeneous and noisy smartphone sensor data.
Machine Learning & AI
6 STUDIESWang 2025: Cardiac Output Prediction
T. Wang et al.
S010 represents a major technical leap for SCG by moving from simple timing estimation to direct Cardiac Output prediction using Deep Learning. Validating against the gold standard RHC in a real-world heart failure cohort makes this a key supporting document for OpenSCG’s clinical monitoring claims.
Tatinati 2014: Wavelet Peak Detection
Sahoo et al.
Foundational technical work on multi-resolution analysis of cardiomechanical signals.
Khosrow-Khavar 2017: Heart Sound Localization
Yu & Liu et al.
Integrates phonocardiography and seismocardiography principles for automated cardiac cycle mapping.
Suresh 2020: SeismoNet End-to-End DL
Prithu Suresh et al.
S027 (SeismoNet) is a key technical reference for OpenSCG's early-stage ML development. It represents the shift from manual feature engineering to end-to-end deep learning, proving that raw SCG vibrations can be directly mapped to cardiac events using modern AI architectures.
Korsgaard 2025: Beat-to-beat Delineation
T. Korsgaard et al.
Introduces and validates a robust deep-learning framework (SeismoTracker) for automated cardiomechanical signal analysis.
Craighero 2024: Cross-dataset SCG Analysis
Craighero et al.
S029 is a critical piece for OpenSCG’s technical strategy. It addresses the 'reality gap'—the fact that models trained on clean lab data often fail in the field. By validating personalization and multi-sensor fusion, it provides a direct scientific foundation for OpenSCG’s 'U-Net v3' and adaptive processing pipeline.
Evidence-Based FAQ
How does respiration affect SCG?
Respiration induces baseline wander and morphology changes. Our algorithms compensate for these effects, though a brief 10-second breath-hold is recommended for highest precision (S039).
Is the technology truly ECG-free?
Yes. Peer-reviewed research confirms the feasibility of identifying heartbeats and inter-beat intervals directly from SCG via template matching and mode decomposition (S036).
Can it monitor Stroke Volume at home?
Studies in diverse patient groups, including children with CHD, have shown that SCG can estimate Stroke Volume with accuracy levels acceptable for clinical monitoring (S034).
Open Research & Collaboration
OpenSCG.org is an open-source initiative. We welcome collaboration with academic institutions, clinical research organizations, and independent developers.