A Cardiac Early Warning System with Multi Channel SCG and ECG Monitoring for Mobile Health
Open accessAbstract
Use of information and communication technology such as smart phone, smart watch, smart glass and portable health monitoring devices for healthcare services has made Mobile Health (mHealth) an emerging research area. Coronary Heart Disease (CHD) is considered as a leading cause of death world wide and an increasing number of people die prematurely due to CHD. Under such circumstances, there is a growing demand for a reliable cardiac monitoring system to catch the intermittent abnormalities and detect critical cardiac behaviors which lead to sudden death. Use of mobile devices to collect Electrocardiography (ECG), Seismocardiography (SCG) data and efficient analysis of those data can monitor a patient’s cardiac activities for early warning. This paper presents a novel cardiac data acquisition method and combined analysis of Electrocardiography (ECG) and multi channel Seismocardiography (SCG) data. An early warning system is implemented to monitor the cardiac activities of a person and accuracy assessment of the early warning system is conducted for the ECG data only. The assessment shows 88% accuracy and effectiveness of our proposed analysis, which implies the viability and applicability of the proposed early warning system.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Signals associated with crying have distinctive frequencies typically between 400 and 500 Hz, with strong harmonics, well separated from cardiac activity (1-50 Hz), muscle tremors (<20 Hz), and operations in care such as patting, rubbing or stroking.
“The signals associated with crying have distinctive frequencies (typically between 400 and 500 Hz, with strong harmonics), well separated from other physiological effects such as cardiac activity (1 – 50 Hz) and muscle tremors (< 20 Hz)43–45 or from various operations in care such as patting, rubbing or stroking (see Supplementary Figs. 24a–c).”
From Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units (2020) · cites this paper as [45]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe two most widely adopted sensor data types are ECG and photoplethysmography (PPG).
“The two most widely adopted sensor data types are ECG and photoplethysmography (PPG) [166–168].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [166]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Procedures for Detecting Outlying Observations in Samples
Grubbs, F. E. (1969) · Technometrics · doi:10.1080/00401706.1969.10490657 · Cited by 4,105 · see citing works
Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards
Baronti, P., Pillai, P., Chook, V., Chessa, S., Gotta, A., & Hu, Y. F. (2006) · Computer Communications · doi:10.1016/j.comcom.2006.12.020 · Cited by 1,431 · see citing works
Wearable Sensors for Remote Health Monitoring
Majumder, S., Mondal, T., & Deen, M. (2017) · Sensors · doi:10.3390/s17010130 · Cited by 1,375 · see citing works
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
Note on a Method for Calculating Corrected Sums of Squares and Products
Welford, B. P. (1962) · Technometrics · doi:10.1080/00401706.1962.10490022 · Cited by 714 · see citing works
Executive Summary: Heart Disease and Stroke Statistics—2015 Update
Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., de Ferranti, S. D., Després, J., Fullerton, H. J., Howard, V. J., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000157 · Cited by 587 · see citing works
Cardiovascular disease in Europe — epidemiological update 2015
Townsend, N. P., Nichols, M. S., Scarborough, P., & Rayner, M. (2015) · European Heart Journal · doi:10.1093/eurheartj/ehv428 · Cited by 568 · see citing works
Heart Rate Assessment Immediately after Birth
Phillipos, E., Solevåg, A. L., Pichler, G., Aziz, K., van Os, S., O’Reilly, M., Cheung, P., & Schmölzer, G. M. (2015) · Neonatology · doi:10.1159/000441940 · Cited by 531 · see citing works
Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction
Gia, T. N., Jiang, M., Rahmani, A.-M., Westerlund, T., Liljeberg, P., & Tenhunen, H. (2015) · doi:10.1109/cit/iucc/dasc/picom.2015.51 · Cited by 448 · see citing works
Smart Vest: Wearable multi-parameter remote physiological monitoring system
Pandian, P. S., Mohanavelu, K., Safeer, K. P., Kotresh, T. M., Shakunthala, D. T., Gopal, P., & Padaki, V. C. (2007) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2007.05.014 · Cited by 420 · see citing works
A wireless wearable ECG sensor for long-term applications
Nemati, E., Deen, M., & Mondal, T. (2012) · IEEE Communications Magazine · doi:10.1109/mcom.2012.6122530 · Cited by 418 · see citing works
Increasing trend of wearables and multimodal interface for human activity monitoring: A review
Kumari, P., Mathew, L., & Syal, P. (2016) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2016.12.001 · Cited by 298 · see citing works
Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring
Spano, E., Pascoli, S. D., & Iannaccone, G. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2564995 · Cited by 256 · see citing works
An autonomic cloud environment for hosting ECG data analysis services
Pandey, S., Voorsluys, W., Niu, S., et al. (2011) · Future Generation Computer Systems · doi:10.1016/j.future.2011.04.022 · Cited by 240 · see citing works
Huang, Y., Hsieh, M.-Y., Chao, H., Hung, S., & Park, J. (2009) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2009.090505 · Cited by 229 · see citing works
A Multiparameter Wearable Physiologic Monitoring System for Space and Terrestrial Applications
Mundt, C. W., Montgomery, K. N., Udoh, U. E., Barker, V. N., Thonier, G. C., Tellier, A. M., Ricks, R. D., Darling, R. B., Cagle, Y. D., Cabrol, N. A., et al. (2005) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2005.854509 · Cited by 226 · see citing works
Normal values of the electrocardiogram for ages 16–90 years
Rijnbeek, P. R., van Herpen, G., Bots, M. L., Man, S., Verweij, N., Albert Hofman, D., Hillege, H. L., Numans, M. E., Swenne, C. A., Witteman, J. C. M., et al. (2014) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2014.07.022 · Cited by 196 · see citing works
Bioluminescent sensor proteins for point-of-care therapeutic drug monitoring
Griss, R., Schena, A., Reymond, L., Patiny, L., Werner, D., Tinberg, C. E., Baker, D. A., & Johnsson, K. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1554 · Cited by 193 · see citing works
Fully Textile, PEDOT:PSS Based Electrodes for Wearable ECG Monitoring Systems
Pani, D., Dessì, A., Saenz-Cogollo, J. F., Barabino, G., Fraboni, B., & Bonfiglio, A. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2465936 · Cited by 193 · see citing works
Relationship between Seismocardiogram and Echocardiogram for Events in the Cardiac Cycle
Crow, R. S., Hannan, P., Jacobs, D., Hedquist, L., & Salerno, D. M. (2017) · American Journal of Noninvasive Cardiology · doi:10.1159/000470156 · Cited by 186 · cited 5× · see citing works
“Moreover, a set of nine inflection points, i.e., AS, MC, AO, RE, AC, MO, RF, IM, IC are also observed in Seismocardiography indicating various unique cardiac mechanical activities [16,17].”
BackgroundResultInfluentialDi Rienzo, M., Vaini, E., Castiglioni, P., Merati, G., Meriggi, P., Parati, G., Faini, A., & Rizzo, F. (2013) · Autonomic Neuroscience · doi:10.1016/j.autneu.2013.04.005 · Cited by 163 · see citing works
Analyzing Healthcare Big Data With Prediction for Future Health Condition
Sahoo, P. K., Mohapatra, S. K., & Wu, S. (2016) · IEEE Access · doi:10.1109/access.2016.2647619 · Cited by 160 · see citing works
Self-citationMiao, F., Cheng, Y., He, Y., He, Q., & Li, Y. (2015) · Sensors · doi:10.3390/s150511465 · Cited by 138 · see citing works
Salerno, D. M., & Zanetti, J. M. (1990) · Cited by 123 · see citing works
Some properties of a simple moving average when applied to forecasting a time series
Johnston, F., Boyland, J. E., Meadows, M., et al. (1999) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2600823 · Cited by 117 · see citing works
Moyer, V. A., & on behalf of the U.S. Preventive Services Task Force (2012) · Annals of Internal Medicine · doi:10.7326/0003-4819-157-7-201210020-00514 · Cited by 104 · see citing works
Economic and biological costs of cardiac imaging
Picano, E. (2005) · Cardiovascular Ultrasound · doi:10.1186/1476-7120-3-13 · Cited by 96 · see citing works
Etemadi, M., Inan, O. T., Heller, J. A., Hersek, S., Klein, L., & Roy, S. (2016) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2015.2405480 · Cited by 95 · cited 1× · see citing works
“Moreover, recent researches [19,20] demonstrate the viability of collection of SCG using convenient body sensor-based wearable devices.”
BackgroundSmart Helmet: Wearable Multichannel ECG and EEG
von Rosenberg, W., Chanwimalueang, T., Goverdovsky, V., et al. (2016) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2016.2609927 · Cited by 92 · see citing works
Textile Technology for the Vital Signs Monitoring in Telemedicine and Extreme Environments
Rienzo, M. D., Meriggi, P., Rizzo, F., Castiglioni, P., Lombardi, C., Ferratini, M., & Parati, G. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2010.2048921 · Cited by 88 · see citing works
KNOWME: a case study in wireless body area sensor network design
Mitra, U., Emken, B. A., Lee, S., et al. (2012) · IEEE Communications Magazine · doi:10.1109/mcom.2012.6194391 · Cited by 82 · see citing works
Unobtrusive and Wearable Systems for Automatic Dietary Monitoring
Prioleau, T., Moore, E., & Ghovanloo, M. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2631246 · Cited by 69 · see citing works
Wireless Body Area Network for Heart Attack Detection [Education Corner]
Wolgast, G., Ehrenborg, C., Israelsson, A., et al. (2016) · IEEE Antennas and Propagation Magazine · doi:10.1109/map.2016.2594004 · Cited by 63 · see citing works
Lin, W.-Y., Tsai, T.-H., Lee, M.-Y., Chou, W.-C., Chang, P.-C., Chou, C.-C., Wen, M.-S., Ho, M.-Y., Lee, W.-C., Hsieh, M.-J., et al. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2620496 · Cited by 62 · see citing works
Self-citationEstimation of hemodynamic parameters from seismocardiogram
Tavakolian, K., Blaber, A. P., Ngai, B., & Kamińska, B. (2010) · Computing in cardiology · Cited by 59 · see citing works
Optimization strategy of Hadoop small file storage for big data in healthcare
He, H. X., Du, Z., Zhang, W., et al. (2015) · The Journal of Supercomputing · doi:10.1007/s11227-015-1462-4 · Cited by 54 · see citing works
Efficient Security Mechanisms for mHealth Applications Using Wireless Body Sensor Networks
Sahoo, P. K. (2012) · Sensors · doi:10.3390/s120912606 · Cited by 54 · see citing works
Self-citationMyocardial contractility: A seismocardiography approach
Tavakolian, K., Portacio, G., Tamddondoust, N. R., et al. (2012) · doi:10.1109/embc.2012.6346795 · Cited by 51 · see citing works
Potential and challenges of body area networks for cardiac monitoring
Gyselinckx, B., Penders, J., & Vullers, R. J. M. (2007) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2007.06.016 · Cited by 47 · see citing works
PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
Miramontes, R., Aquino-Santos, R., Flores, A., et al. (2017) · Sensors · doi:10.3390/s17010176 · Cited by 43 · see citing works
Influences of lifestyle factors on cardiac autonomic nervous system activity over time
Hu, M. X., Lamers, F., de Geus, E. J. C., & Penninx, B. W. J. H. (2016) · Preventive Medicine · doi:10.1016/j.ypmed.2016.11.003 · Cited by 33 · see citing works
An Open Source ECG Clock Generator for Visualization of Long-Term Cardiac Monitoring Data
Page, A., Soyata, T., Couderc, J., & Aktaş, M. K. (2015) · IEEE Access · doi:10.1109/access.2015.2509426 · Cited by 28 · see citing works
A single-chip encrypted wireless 12-lead ECG smart shirt for continuous health monitoring
Morrison, T., Silver, J., & Otis, B. P. (2014) · Symposium on VLSI Circuits · doi:10.1109/vlsic.2014.6858433 · Cited by 26 · see citing works
Mayer, F., Stahrenberg, R., Gröschel, K., Mostardt, S., Biermann, J., Edelmann, F., Liman, J., Wasem, J., Goehler, A., Wachter, R., et al. (2013) · Clinical Research in Cardiology · doi:10.1007/s00392-013-0601-2 · Cited by 17 · see citing works
Embedded real-time ECG delineation methods: A comparative evaluation
Braojos, R., Ansaloni, G., Atienza, D., et al. (2012) · doi:10.1109/bibe.2012.6399715 · Cited by 16 · see citing works
Risk Stratification for Hospital Readmission of Heart Failure Patients
Hon, C. P., Pereira, M. W., Sushmita, S., Teredesai, A. M., & Cock, M. D. (2016) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2975167.2985648 · Cited by 7 · see citing works
Wearable Data Acquisition System of Multimodal Physiological Signals for Personal Health Care
Annus, P., Samieipour, A., Marek, R., et al. (2013) · Studies in health technology and informatics · doi:10.3233/978-1-61499-268-4-107 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [42]Peng M. Wang T. Hu G. Zhang H. A wearable heart rate belt for ambulant ECG monitoring Proceedings of the 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom) Beijing, China 10–13 October 2012 371 374
Cited by
Sorted by citations
Chung, H. U., Rwei, A. Y., Hourlier-Fargette, A., Xu, S., Lee, K., Dunne, E. C., Xie, Z., Liu, C., Carlini, A., Kim, D. H., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0792-9 · Cited by 482 · see citing works
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
- Authors
- Sahoo, P., Thakkar, H., & Lee, M.-Y.
- Affiliations
- Chang Gung University (TW)
- Corresponding author
- Lee, Ming-Yih
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-03-29
- Volume
- 17
- Issue
- 4
- Pages
- 711
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5421671
- OpenAlex
- W2603156040
- Cited by
- 81
- References
- 47
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 5 · 2023: 7 · 2022: 18 · 2021: 10 · 2020: 17 · 2019: 8 · 2018: 6 · 2017: 2
- FWCI
- 6.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology (MOST), Taiwan (105-2627-E-182-001, 105-2221-E-182-050)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Keywords
- seismocardiography, electrocardiogram, mobile health, coronary artery disease, cardiac monitoring, wearable health monitoring
- MeSH terms
- Smartphone, Arrhythmias, Cardiac, Electrocardiography, Heart, Humans, Telemedicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
