journal article·2019-06-14Open access

Inexpensive and flexible nanographene-based electrodes for ubiquitous electrocardiogram monitoring

Francisco J. Romero, Encarnacion Castillo, Almudena Rivadeneyra, Alejandro Toral-Lopez, Markus Becherer, Francisco G. Ruiz, Noel Rodriguez, Diego P. Morales

npj Flexible Electronics· Volume 3· Issue 1

Abstract

Abstract Flexible electronics is one of the fundamental technologies for the development of electronic skin, implant wearables, or ubiquitous biosensing. In this context, graphene-derived materials have attracted great interest due to their unique properties to fulfill the demands of these applications. Here we report a simple one-step method for the fabrication of electrophysical electrodes based on the photothermal production of porous nanographene structures on the surface of flexible polyimide substrates. This approach constitutes an inexpensive alternative to the commercial medical electrodes, leading to a lower and much more stable skin–electrode contact resistance and providing comparable signal transduction. This technology has been framed inside the IoT paradigm through the development of a denoising and signal classification clustering algorithm suitable for its implementation in wearable devices. The experiments have shown promising achievements regarding noise reduction, increasing the crest factor ~3.7 dB, as well as for the over 90% heart rate-monitoring accuracy.
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Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications · Neuroscience and Neural Engineering

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References (40)

Show source-provided references
1. Domanski, M. J. et al. Next steps in primary prevention of coronary heart disease. J. Am. Coll. Cardiol. 66, 1828–1836 (2015).10.1016/j.jacc.2015.08.857 ↗
2. Buse, J. B. et al. Primary prevention of cardiovascular diseases in people with diabetes mellitus. Circulation 115, 114–126 (2007).10.1161/CIRCULATIONAHA.106.179294 ↗
3. Wilkins, E. et al. European Cardiovascular Disease Statistics (European Heart Network, Brussels, Belgium, 2017).
4. De Bacquer, D., De Backer, G., Kornitzer, M. & Blackburn, H. Prognostic value of ECG findings for total, cardiovascular disease, and coronary heart disease death in men and women. Heart 80, 570–577 (1998).10.1136/hrt.80.6.570 ↗
5. Majumder, S., Mondal, T. & Deen, M. J. Wearable sensors for remote health monitoring. Sensors 17, 1–45 (2017).10.1109/JSEN.2017.2761499 ↗
6. Kenry, Yeo, J. C. & Lim, C. T. Emerging flexible and wearable physical sensing platforms for healthcare and biomedical applications. Microsyst. Nanoeng. 2, 16043 (2016).10.1038/micronano.2016.43 ↗
7. Castano, L. M. & Flatau, A. B. Smart fabric sensors and e-textile technologies: a review. Smart Mater. Struct. 23, 053001 (2014).10.1088/0964-1726/23/5/053001 ↗
8. Zama, T. & Shimada, S. Simultaneous measurement of electroencephalography and near-infrared spectroscopy during voluntary motor preparation. Sci. Rep. 5, 16438 (2015).10.1038/srep16438 ↗
9. Baek, J.-Y., An, J.-H., Choi, J.-M., Park, K.-S. & Lee, S.-H. Flexible polymeric dry electrodes for the long-term monitoring of ECG. Sens. Actuators A 143, 423–429 (2008).10.1016/j.sna.2007.11.019 ↗
10. Gruetzmann, A., Hansen, S. & Mller, J. Novel dry electrodes for ECG monitoring. Physiol. Meas. 28, 1375 (2007).10.1088/0967-3334/28/11/005 ↗
11. Kuniharu, T., Wataru, H., Shingo, H., Takayuki, A. & Seiji, A. Toward flexible and wearable human-interactive health-monitoring devices. Adv. Healthc. Mater. 4, 487–500 (2014).
12. Misra, V. et al. Flexible technologies for self-powered wearable health and environmental sensing. Proc. IEEE 103, 665–681 (2015).10.1109/JPROC.2015.2412493 ↗
13. Liu, L., Yu, Y., Yan, C., Li, K. & Zheng, Z. Wearable energy-dense and power-dense supercapacitor yarns enabled by scalable graphenemetallic textile composite electrodes. Nat. Commun. 6, 7260 (2015).10.1038/ncomms8260 ↗
14. Vargas-Quesada, B., Chinchilla-Rodrguez, Z. & Rodriguez, N. Identification and visualization of the intellectual structure in graphene research. Front. Res. Metr. Anal. 2, 7 (2017).10.3389/frma.2017.00007 ↗
15. Celik, N., Manivannan, N., Strudwick, A. & Balachandran, W. Graphene-enabled electrodes for electrocardiogram monitoring. Nanomaterials 6, 156 (2016).10.3390/nano6090156 ↗
16. Lou, C. et al. Flexible graphene electrodes for prolonged dynamic ECG monitoring. Sensors 16, 1833 (2016).10.3390/s16111833 ↗
17. Karim, N. et al. All inkjet-printed graphene-based conductive patterns for wearable e-textile applications. J. Mater. Chem. C 5, 11640–11648 (2017).10.1039/C7TC03669H ↗
18. Jung, H. et al. CNT/PDMS composite flexible dry electrodesfor long-term ECG monitoring. IEEE Trans. Biomed. Eng. 59, 1472–1479 (2012).10.1109/TBME.2012.2190288 ↗
19. Jiao, L. et al. Laser-induced graphene on additive manufacturing parts. Nanomaterials 9, 90 (2019).10.3390/nano9010090 ↗
20. Romero, F. J. et al. In-depth study of laser diode ablation of kapton polyimide for flexible conductive substrates. Nanomaterials 8, 517 (2018).10.3390/nano8070517 ↗
21. Romero, F. J. et al. Design guidelines of laser reduced graphene oxide conformal thermistor for IoT applications. Sens. Actuators A 274, 148–154 (2018).10.1016/j.sna.2018.03.014 ↗
22. Duy, L. X. et al. Laser-induced graphene fibers. Carbon 126, 472–479 (2018).10.1016/j.carbon.2017.10.036 ↗
23. Lin, J. et al. Laser-induced porous graphene films from commercial polymers. Nat. Commun. 5, 5714 (2014).10.1038/ncomms6714 ↗
24. Wu, J.-B., Lin, M.-L., Cong, X., Liu, H.-N. & Tan, P.-H. Raman spectroscopy of graphene-based materials and its applications in related devices. Chem. Soc. Rev. 47, 1822–1873 (2018).10.1039/C6CS00915H ↗
25. Meziane, N., Webster, J. G., Attari, M. & Nimunkar, A. J. Dry electrodes for electrocardiography. Physiol. Meas. 34, R47–R69 (2013).10.1088/0967-3334/34/9/R47 ↗
26. Elgendi, M., Mohamed, A. & Ward, R. Efficient ECG compression and QRS detection for e-health applications. Sci. Rep. 7, 459 (2017).10.1038/s41598-017-00540-x ↗
27. Barold, S. S. Willem einthoven and the birth of clinical electrocardiography a hundred years ago. Card. Electrophysiol. Rev. 7, 99–104 (2003).10.1023/A:1023667812925 ↗
28. Encarnacion Castillo, D. P. M., Garcia, A., Parrilla, L., Ruiz, V. U. & Alvarez-Bermejo, J. A. A clustering-based method for single-channel fetal heart rate monitoring. PLoS ONE 13, e0199308 (2018).10.1371/journal.pone.0199308 ↗
29. Xia, Y. et al. An automatic cardiac arrhythmia classification system with wearable electrocardiogram. IEEE Access 6, 16529–16538 (2018).10.1109/ACCESS.2018.2807700 ↗
30. Zhang, Q., Zhou, D. & Zeng, X. Heartid: a multiresolution convolutional neural network for ecg-based biometric human identification in smart health applications. IEEE Access 5, 11805–11816 (2017).10.1109/ACCESS.2017.2707460 ↗
31. Toral, V. et al. Wearable system for biosignal acquisition and monitoring based on reconfigurable technologies. Sensors 19, 1590 (2019).10.3390/s19071590 ↗
32. Ferrari, A. & Basko, D. Raman spectroscopy as a versatile tool for studying the properties of graphene. Nat. Nanotechnol. 8, 235–246 (2013).10.1038/nnano.2013.46 ↗
33. Spach, M. S., Barr, R. C., Havstad, J. W. & Long, E. C. Skin–electrode impedance and its effect on recording cardiac potentials. Circulation 34, 649–656 (1966).10.1161/01.CIR.34.4.649 ↗
34. Takamatsu, S. et al. Direct patterning of organic conductors on knitted textiles for long-term electrocardiography. Sci. Rep. 5, 15003 (2015).10.1038/srep15003 ↗
35. Satija, U., Ramkumar, B. & Manikandan, M. S. Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE J. Biomed. Health Inform. 22, 722–732 (2018).10.1109/JBHI.2017.2686436 ↗
36. Castillo, E. et al. Efficient wavelet-based ECG processing for single-lead FHR extraction. Digit. Signal Process. 23, 1897–1909 (2013).10.1016/j.dsp.2013.07.010 ↗
37. Mamaghanian, H., Khaled, N., Atienza, D. & Vandergheynst, P. Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Trans. Biomed. Eng. 58, 2456–2466 (2011).10.1109/TBME.2011.2156795 ↗
38. Sharma, L., Dandapat, S. & Mahanta, A. ECG signal denoising using higher order statistics in wavelet subbands. Biomed. Signal Process. Control 5, 214–222 (2010).10.1016/j.bspc.2010.03.003 ↗
39. Castillo, E. et al. Noise suppression in ECG signals through efficient one-step wavelet processing techniques. J. Appl. Math. 2013, 1–13 (2013).10.1155/2013/763903 ↗
40. Zidelmal, Z., Amirou, A., Adnane, M. & Belouchrani, A. QRS detection based on wavelet coefficients. Comput. Methods Prog. Biomed. 107, 490–496 (2012).10.1016/j.cmpb.2011.12.004 ↗