A Unified Framework for Quality Indexing and Classification of Seismocardiogram Signals
Open accessAbstract
The seismocardiogram (SCG) is a noninvasively-obtained cardiovascular bio-signal that has gained traction in recent years, however is limited by its susceptibility to noise and motion artifacts. Because of this, signal quality must be assured before data are used to inform clinical care. Common methods of signal quality assurance include signal classification or assignment of a numerical quality index. Such tasks are difficult with SCG because there is no accepted standard for signal morphology. In this paper, we propose a unified method of quality indexing and classification that uses multi-subject-based methods to overcome this challenge. Dynamic-time feature matching is introduced as a novel method of obtaining the distance between a signal and reference template, with this metric, the signal quality index (SQI) is defined as a function of the inverse distance between the SCG and a large set of template signals. We demonstrate that this method is able to stratify SCG signals on held-out subjects based on their level of motion-artifact corruption. This method is extended, using the SQI as a feature for classification by ensembled quadratic discriminant analysis. Classification is validated by demonstrating, for the first time, both detection and localization of SCG sensor misplacement, achieving an F1 score of 0.83 on held-out subjects. This paper may provide a necessary step toward automating the analysis of SCG signals, addressing many of the key limitations and concerns precluding the method from being widely used in clinical and physiological sensing applications.
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A signal quality indexing (SQI) method was applied to SCG beats to exclude beats corrupted by noise above a threshold.
“we apply a signal quality indexing (SQI) method for SCG beats to exclude the beats corrupted by noise above a certain threshold. [45]”
From A Chest‐Conformable, Wireless Electro‐Mechanical E‐Tattoo for Measuring Multiple Cardiac Time Intervals (2023) · cites this paper as [45]
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“We did not use 201 a universal hard threshold for the SQI cutoff score for two 202 reasons.”
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“The SQI used in this work — described in detail in [11] — computes the signal quality of each signal segment as its average distance from a set of templates.”
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Pilar, M. A. I. D., Romano, C., Pinnelli, M., Nusca, A., Ussia, G. P., Schena, E., & Massaroni, C. (2025) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot66048.2025.11122086 · Cited by 2 · see citing works
“Traditional methods for assessing signal quality often rely on time-domain or frequency-domain analyses, which may not fully encapsulate the complex dynamics of SCG waveforms [4], [5], [6], [7], [8].”
Nikbakht, M. R., Sanchez-Perez, J. A., Aljiffry, A., Maher, K. O., Inan, O. T., & Rodriguez, Z. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3371354 · Cited by 2 · see citing works
“This SQI method was designed for Seismocardiogram (SCG) signals, however, since heart sounds are similar to SCG signals but contain a broader spectrum (SCG being in the 1–40 Hz region), using the same SQI algorithm seems appropriate [35], [38].”
MethodSelf-citationChen, C., Lin, W., & Lee, M. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3322675 · Cited by 1 · see citing works
“Because dynamic time warping (DTW) has been used to measure the distance between an SCG signal and a reference template [29], the present study leveraged a variant method combining the DTW method with visual inspection and comparison, known as DTW-based quasi-synchronous alignment [30], to consider…”
MethodRobustness of Persistence Diagrams to Time-Delay for Seismocardiogram Signal Quality Assessment*
Nawar, A., Rahman, F. N., Kilic, O., Shah, A. J., & Inan, O. T. (2025) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn66969.2025.11337812 · Cited by 1 · see citing works
“The DTFM distance metric, defined as the minimum Euclidean distance between warped template and target signals after feature mapping, is described in detail by Zia et al [4].”
InfluentialSelf-citationQuantifying changes in triaxial seismocardiography variability due to sub-optimal volume status
Cestero, G. I., Bouzid, Z., Nawar, A., Yaldiz, C. O., Tangolar, D., Wu, Z. J., Hahn, J., & Inan, O. T. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1823345 · Cited by 1 · see citing works
Self-citationLocalizing Placement of Cardiomechanical Sensors during Dynamic Periods via Template Matching
Zia, J. S., Kimball, J. P., & Inan, O. T. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176732 · Cited by 1 · see citing works
“Originally proposed in [5], the SQI used in this work computes the distance between a signal vector s ∈ R and template vector t ∈ R as”
BackgroundMethodResultInfluentialSelf-citationSeismocardiography Pig Hypovolemia Dataset for Signal Quality Indexing and Validated Cardiac Timings
Cho, M. J., Yaldiz, C. O., Nawar, A., Abbaraju, V., Emadi, R., Kilic, O. S., Bouzid, Z., Rahman, F. N., Chen, C., Cook, J. M., et al. (2026) · Scientific Data · doi:10.1038/s41597-026-06733-2 · Cited by 1 · see citing works
“Yet, manual annotations remain the best available reference standard in determining signal quality indices (SQI) and fiducial point locations for SCG signals 22 .”
Self-citationZhou, Y., Mousavi, A. S., Chalumuri, Y. R., Parreira, J. D., Modak, M., Sanchez-Perez, J. A., Gazi, A., Inan, O. T., & Hahn, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4591656
Self-citationSCG systolic detection in the wild: A static–dynamic cross-dataset analysis
Craighero, M., Solbiati, S., Mozzini, F., Caiani, E. G., & Boracchi, G. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111297
Sahoo, S., & Jain, P. K. (2026) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-026-01741-3
“Zia et al. used an ensembled quadratic discriminant analysis method to calculate the Signal Quality Index (SQI) and then detect sensor misplacements using dynamic-time feature matching [12 ].”
Cook, J. M., Chen, C., Inan, F. B., Nichols, C. J., Rahman, F. N., Nawar, A., Reichelt, L. J., Cho, M., Sangar, Y., Hester, J., et al. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3702932
Self-citationTowards Monitoring Acute Opioid Withdrawal Severity Using a Multimodal Wearable Chest Patch
Abbaraju, V., Rahman, F. N., Cho, M. J., Liu, J. C., Yang, Y., Murray, C. E., Nawar, A., Berkebile, J. A., Chiok, J., Choi, J., et al. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3833425
Self-citationMorphological Robustness Analysis of Seismocardiogram SPAR Features across Breathing Conditions
Pilar, M. A. I. D., Massaroni, C., Schena, E., & Presti, D. L. (2026) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot69397.2026.11653197
“Conventional SCG analysis relies predominantly on time-domain or frequency-domain descriptors [6], [7], [8], [9].”
MIMO mmWave Radar-Derived Seismocardiography for Contactless Cardiac Dynamics Monitoring
Lin, Y.-J., Chang, R.-J., Chen, Y.-L., Chen-Lin, Jeng, G.-S., & Lo, M.-T. (2026) · IEEE International Symposium on Radio-Frequency Integration Technology (RFIT) · doi:10.1109/rfit69646.2026.11711967
- Authors
- Zia, J., Kimball, J., Hersek, S., Shandhi, M. M. H., Semiz, B., & Inan, O. T.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2020-04
- Volume
- 24
- Issue
- 4
- Pages
- 1080-1092
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- PMC
- PMC7193993
- OpenAlex
- W2966539497
- Cited by
- 48
- References
- 38
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 4 · 2023: 8 · 2022: 7 · 2021: 6 · 2020: 8
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (1R01HL130619-A1, UL1TR002378)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- seismocardiogram, signal quality index, signal classification, motion artifacts, quadratic discriminant analysis, noise robustness
- MeSH terms
- Adult, Algorithms, Female, Heart, Heart Function Tests, Humans, Male, Signal Processing, Computer-Assisted, Young Adult
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