A Synthetic Seismocardiogram and Electrocardiogram Generator Phantom
Abstract
The seismocardiogram (SCG) and electrocardio-gram (ECG) signals are two signals of cardiovascular origin containing important features for cardiac health assessment. Effective use of these signals requires recordings with acceptable signal to noise ratio. Studying the effects of external factors such as vibrations on these signals, and subsequent artifact removal algorithm design, remains a challenge due to lack of access to ground truth labels and human participant safety concerns. In this work, a synthetic SCG and ECG generator system is presented that enables data collection in environments that may be unsafe or inconvenient for human participants and offers ground truth labels along with the simulated recordings. The system was validated using real human SCG and ECG signals and showed >90%, and >98% input output correlations in both time and frequency domains for SCG and ECG signals respectively. Thus, the system is able to generate realistic SCG and ECG signals with clinically relevant amplitudes favorable for participant-free data collection in relevant environments.
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Both synthetic and real human SCG signals fed into the phantom generator were accurately reproduced by the e-tattoo.
“Both synthetic SCG signals (Figure 4g) and real human SCG signals (Figure 4h) were fed into the generator and were accurately reproduced by the e-tattoo. [40,41]”
From A Chest‐Conformable, Wireless Electro‐Mechanical E‐Tattoo for Measuring Multiple Cardiac Time Intervals (2023) · cites this paper as [40]
A synthetic voltage and vibration-generating phantom was re-implemented to replicate human-like ECG and SCG signals for testing the SCG sensor.
“we re-implemented a synthetic voltage and vibration-generating phantom for replicating human-like ECG and SCG signals from our prior work. [40]”
From A Chest‐Conformable, Wireless Electro‐Mechanical E‐Tattoo for Measuring Multiple Cardiac Time Intervals (2023) · cites this paper as [40]
Cites
Ballistocardiography and Seismocardiography: A Review of Recent Advances
Inan, O. T., Migeotte, P.-F., Park, K.-S., Etemadi, M., Tavakolian, K., Casanella, R., Zanetti, J., Tank, J., Funtova, I., Prisk, G. K., et al. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2361732 · Cited by 775 · see citing works
Self-citationAutomatic Identification of Systolic Time Intervals in Seismocardiogram
Shafiq, G., Tatinati, S., Ang, W. T., & Veluvolu, K. C. (2016) · Scientific Reports · doi:10.1038/srep37524 · Cited by 68 · see citing works
Vibrations transmitted from human hands to upper arm, shoulder, back, neck, and head
Xu, X. S., Dong, R. G., Welcome, D. E., Warren, C. M., McDowell, T. W., & Wu, J. Z. (2016) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2016.07.001 · Cited by 58 · see citing works
Shandhi, M. M. H., Fan, J., Heller, J. A., Etemadi, M., Klein, L., & Inan, O. T. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3147066 · Cited by 57 · see citing works
Self-citationEnabling the assessment of trauma-induced hemorrhage via smart wearable systems
Zia, J. S., Kimball, J. P., Rolfes, C., Hahn, J., & Inan, O. T. (2020) · Science Advances · doi:10.1126/sciadv.abb1708 · Cited by 52 · see citing works
Self-citationTissue Phantoms for Biomedical Applications in Raman Spectroscopy: A Review
Vardaki, M. Z., & Kourkoumelis, N. (2020) · Biomedical Engineering and Computational Biology · doi:10.1177/1179597220948100 · Cited by 48 · see citing works
Shandhi, M. M. H., Bartlett, W. H., Heller, J. A., Etemadi, M., Young, A. J., Plötz, T., & Inan, O. T. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3009903 · Cited by 44 · see citing works
Self-citationReducing the Impact of External Vibrations on Fiducial Point Detection in Seismocardiogram Signals
Lin, D. J., Kimball, J. P., Zia, J. S., Ganti, V. G., & Inan, O. T. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3090376 · Cited by 39 · see citing works
Self-citationEnabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum
Chan, M., Ganti, V. G., Heller, J. A., Abdallah, C. A., Etemadi, M., & Inan, O. T. (2021) · Biosensors · doi:10.3390/bios11120521 · Cited by 38 · see citing works
Self-citation$H_alpha $ —A Consistent Estimator for Frequency Response Functions With Input and Output Noise
Antoni, J., Wagstaff, P., & HENRIO, J. (2004) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2004.823314 · Cited by 30 · see citing works
Hersek, S., Semiz, B., Shandhi, M. M. H., Orlandic, L., & Inan, O. T. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2931872 · Cited by 30 · see citing works
Self-citationShen, S., Wang, H., Xue, Y. K., Li, Y., Zhou, X., Zhao, Z., Dong, E., Liu, B., Liu, W., Cromeens, B. P., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-08579-6 · Cited by 27 · see citing works
Taebi, A., & Mansy, H. A. (2017) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb.2017.8257022 · Cited by 26 · see citing works
An ECG simulator with a novel ECG profile for physiological signals
Edelmann, J.-C., Mair, D., Ziesel, D., Burtscher, M., & Ussmueller, T. (2018) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2019.1576788 · Cited by 22 · see citing works
Synthetic seismocardiogram generation using a transformer-based neural network
Nikbakht, M. R., Gazi, A. H., Zia, J. S., An, S., Lin, D. J., Inan, O. T., & Kamaleswaran, R. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad067 · Cited by 10 · see citing works
Self-citationAllemang, R. J., Brown, D. L., & Rost, R. (1987) · Defense Technical Information Center (DTIC) · Cited by 1 · see citing works
Flagged first, then by citations
3 of the 19 references couldn’t be matched to a known paper.
Cited by
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Bhattacharya, S., Nikbakht, M., Alden, A., Tan, P., Wang, J., Alhalimi, T. A., Kim, S., Wang, P., Tanaka, H., Tandon, A., et al. (2023) · Advanced Electronic Materials · doi:10.1002/aelm.202201284 · Cited by 42 · see citing works
Self-citationA Residual U-Net Neural Network for Seismocardiogram Denoising and Analysis During Physical Activity
Nikbakht, M. R., Chan, M., Lin, D. J., Gazi, A. H., & Inan, O. T. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3392532 · Cited by 15 · see citing works
“…a more realistic mixture of walking noise and SCG signals in presence of different noise modalities while having access to clean ground truth [64]; and (4) In this study, we utilized a dataset derived from recordings of 20 human participants using the novel multi-node wearable device, and we…”
MethodSelf-citationSynthetic seismocardiogram generation using a transformer-based neural network
Nikbakht, M. R., Gazi, A. H., Zia, J. S., An, S., Lin, D. J., Inan, O. T., & Kamaleswaran, R. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad067 · Cited by 10 · see citing works
Self-citationSeismoNet: A Multi-Node Wireless Wearable Platform for Enhanced Physiological Sensing
Nikbakht, M. R., Chan, M., Lin, D. J., Nichols, C. J., Bibidakis, M., Soliman, M. M., & Inan, O. T. (2023) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn58485.2023.10331306 · Cited by 5 · see citing works
“However, actionable health assessment requires accurate and reliable feature extraction which is often challenging when using SCG signals corrupted by noise and motion artifacts [6].”
MethodSelf-citationLearning Seismocardiogram Beat Denoising Without Clean Data
Nikbakht, M. R., Lin, D. J., & Inan, O. T. (2023) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi58575.2023.10313428 · Cited by 4 · see citing works
“Future work can explore ways to create naturally mixed signal and noise using systems that can replicate the SCG in noisy environments [18].”
BackgroundSelf-citationGenerating Synthetic Mechanocardiograms for Machine Learning-Based Peak Detection
Sandelin, J., Elnaggar, I., Lahdenoja, O., Kaisti, M., & Koivisto, T. (2024) · IEEE Sensors Letters · doi:10.1109/lsens.2024.3443526 · Cited by 3 · see citing works
Lin, D. J., Nikbakht, M. R., Lewis, R., Medda, A., & Inan, O. T. (2023) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn58485.2023.10331421 · Cited by 1 · see citing works
“The SCG waveform was synthetically created using a generator system previously developed by Nikbakht et al. [12], which replicates input SCG sequences using an exciter speaker.”
MethodSelf-citationHeart Rate Estimation using Mobile Seismocardiogram (m-SCG)
Rajeshwari, B. S., Harshitha, B., Chandan, N. N., Babu, S. D. P., & Mahalakshmi, G. (2024) · International Conference on Smart Systems for Applications in Electrical Sciences (ICSSES) · doi:10.1109/icsses62373.2024.10561284
Design of ECG Signal Generator
Kusuma, E. D., Wicaksono, R., & Pratama, A. R. (2025) · doi:10.1109/icitee66631.2025.11338281
“Previous research has utilized more microcontroller units [3], [5], [6] compared to computer processor units [4] due to their compactness.”
- Authors
- Nikbakht, M., Lin, D. J., Gazi, A. H., & Inan, O. T.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- 2022 IEEE Sensors
- Publisher
- IEEE
- Published
- 2022-10-30
- Pages
- 1-4
- ISSN
- 1930-0395 (ISSN-L)
- OpenAlex
- W4311414010
- Cited by
- 9
- References
- 19
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 5
- FWCI
- 4.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... (publishedVersion)
- Funders
- Office of Naval Research (N00014-21-1-2036)
- National Science Foundation (DGE-2039655)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Keywords
- seismocardiogram, electrocardiogram, synthetic signal generation, ground truth labels, artifact removal, cardiovascular signals
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