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Erschienen in:
Reduced Graphene Oxide for the Design of Electrocardiogram Sensors: Current Status and Perspectives Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

18. Self-Powered SoC Platform for Wearable Health Care

verfasst von : Mohammad Alhawari, Dima Kilani, Temesghen Habte, Yonatan Kifle, Nourhan Bayasi, Ismail Elnaggar, Nicholas Halfors, Baker Mohammad, Hani Saleh, Mohammed Ismail

Erschienen in: The IoT Physical Layer

Verlag: Springer International Publishing

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Abstract

This chapter presents a top-level design of the first self-powered SoC platform that can predict, with high accuracy, ventricular arrhythmia before it occurs. The system provides a very high level of integration in a single chip of mainstream modules that are typically needed to build biomedical devices. Hence, the platform could help in reducing the cost in designing not only for ECG monitoring systems, but for generic low-power health care devices. The platform consists of a graphene-based sensors to acquire ECG signals, an analog front-end to amplify and digitize the ECG, a custom processor to perform feature extraction and classification, a wireless transmitter to send the data to a point of care, and an energy harvesting unit to power the whole system. The platform consumes very low power that can be completely powered by the thermal energy generated from the human body. The system is imagined to be integrated within a necklace which can be worn by a patient comfortably. Hence, it can provide a continuous monitoring of the patient’s condition and connect him directly to his doctor for immediate attention if necessary.

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Vorheriges Kapitel Macromodeling of Microbatteries for IoT Micropower Source Integration
Nächstes Kapitel Toward an Integrated, Low-Power Platform for Continuous Congestive Heart Failure Monitoring
Literatur
1.
E. Nemati, M.J. Deen, T. Mondal, A wireless wearable ECG sensor for long-term applications. IEEE Commun. Mag. 50(1), 36–43 (2012)CrossRef
2.
Y.M. Chi, T.-P. Jung, G. Cauwenberghs, Dry-contact and noncontact biopotential electrodes: methodological review. IEEE Rev. Biomed. Eng. 3, 106–119 (2010)CrossRef
3.
J.-Y. Baek, J.-H. An, J.-M. Choi, K.-S. Park, S.-H. Lee, Flexible polymeric dry electrodes for the long-term monitoring of ecg. Sens. Actuators A: Phys. 143(2), 423–429 (2008)CrossRef
4.
Y.A. Samad, Y. Li, S.M. Alhassan, K. Liao, Non-destroyable graphene cladding on a range of textile and other fibers and fiber mats. RSC Adv. 4(33), 16935–16938 (2014)CrossRef
5.
M.K. Yapici, T. Alkhidir, Y.A. Samad, K. Liao, Graphene-clad textile electrodes for electrocardiogram monitoring. Sens. Actuators B: Chem. 221, 1469–1474 (2015)CrossRef
6.
C. Lu, V. Raghunathan, K. Roy, Efficient design of micro-scale energy harvesting systems. IEEE J. Emerg. Sel. Top. Circuits Syst. 1, 254–266 (2011)CrossRef
7.
P.-S. Weng, H.-Y. Tang, P.-C. Ku, L.-H. Lu, 50 mv-input batteryless boost converter for thermal energy harvesting. IEEE J. Solid-State Circuits 48(4), 1031–1041 (2013)CrossRef
8.
Y. Ramadass, A. Chandrakasan, A battery-less thermoelectric energy harvesting interface circuit with 35 mv startup voltage. IEEE J. Solid-State Circuits 46(1), 333–341 (2011)CrossRef
9.
P. Luo, S. Zhen, J. Wang, K. Yang, P. Liao, X. Zhu, Digital assistant power integrated technologies for pmu in scaling cmos process. IEEE Trans. Power Electron. 29(7), 3798–3807 (2014)CrossRef
10.
L.G. Salem, P.P. Mercier, An 85%-efficiency fully integrated 15-ratio recursive switched-capacitor dc-dc converter with 0.1-to-2.2 v output voltage range, in ISSCC (IEEE, 2014), pp. 88–89
11.
J. Pyo, Y. Shin et al., 20nm high-k metal-gate heterogeneous 64b quad-core cpus and hexa-core gpu for high-performance and energy-efficient mobile application processor, in ISSCC (IEEE, 2015), pp. 1–3
12.
H. Singh, K. Agarwal, D. Sylvester, K.J. Nowka, Enhanced leakage reduction techniques using intermediate strength power gating. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 15(11), 1215–1224 (2007)CrossRef
13.
M. Alioto, Ultra-low power VLSI circuit design demystified and explained. Trans. Circuits Syst. I 59(1), 3–29 (2012)MathSciNetCrossRef
14.
N. Bayasi, T. Tekeste, H. Saleh, B. Mohammad, A. Khandoker, M. Ismail, Low-power ecg-based processor for predicting ventricular arrhythmia. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 99, 1–13 (2015)
15.
E. Carlson, K. Strunz, B. Otis, A 20 mv input boost converter with efficient digital control for thermoelectric energy harvesting. IEEE J. Solid-State Circuits 45(4), 741–750 (2010)CrossRef
16.
Q. Fan, F. Sebastianen, H. Huijsing, K. Makinwa, A 2.1 \(\mu \)w area-efficient capacitively-coupled chopper instrumentation amplifier for ecg applications in 65 nm cmos, in 2010 IEEE Asian Solid State Circuits Conference (A-SSCC) (IEEE, 2010), pp. 1–4
17.
Y.-P. Chen, D. Jeon, Y. Lee, Y. Kim, Z. Foo, I. Lee, N.B. Langhals, G. Kruger, H. Oral, O. Berenfeld et al., An injectable 64 nw ecg mixed-signal soc in 65 nm for arrhythmia monitoring. IEEE J. Solid-State Circuits 50(1), 375–390 (2015)CrossRef
18.
X. Zou, X. Xu, J. Tan, L. Yao, Y. Lian, A 1-v 1.1-\(\mu \)w sensor interface ic for wearable biomedical devices, in 2008 IEEE International Symposium on Circuits and Systems (IEEE, 2008), pp. 2725–2728
19.
X. Zou, X. Xu, L. Yao, Y. Lian, A 1-v 450-nw fully integrated programmable biomedical sensor interface chip. IEEE J. Solid-State Circuits 44(4), 1067–1077 (2009)CrossRef
20.
X. Pu, L. Wan, Y. Sheng, P. Chiang, Y. Qin, Z. Hong, A wireless 8-channel ecg biopotential acquisition system for dry electrodes, in 2012 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT) (IEEE, 2012), pp. 140–142
21.
C.-Y. Chiang, H.-H. Chen, T.-C. Chen, C.-S. Liu, Y.-J. Huang, S.-S. Lu, C.-W. Lin, L.-G. Chen, Analysis and design of on-sensor ecg processors for realtime detection of vf, vt, and pvc, in 2010 IEEE Workshop on Signal Processing Systems (SIPS) (2010), pp. 42–45
22.
Q. Li, C. Rajagopalan, G. Clifford, Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans. Biomed. Eng. 61, 1607–1613 (2014). JuneCrossRef
23.
J. Martinez, R. Almeida et al., A wavelet-based ecg delineator: evaluation on standard database. IEEE Trans. Biomed. Eng. 51(4), 570–581 (2004)
24.
N. Bayasi, T. Tekeste, H. Saleh, A. Khandoker, B. Mohammad, M. Ismail, Adaptive technique for p and t wave delineation in electrocardiogram signals, in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2014), pp. 90–93
25.
T. Tekeste, N. Bayasi, H. Saleh, A. Khandoker, B. Mohammad, M. Al-Qutayri, M. Ismail, Adaptive ecg interval extraction, in 2015 IEEE International Symposium on Circuits and Systems (ISCAS) (IEEE, 2015), pp. 998–1001
Metadaten
Titel
Self-Powered SoC Platform for Wearable Health Care
verfasst von
Mohammad Alhawari
Dima Kilani
Temesghen Habte
Yonatan Kifle
Nourhan Bayasi
Ismail Elnaggar
Nicholas Halfors
Baker Mohammad
Hani Saleh
Mohammed Ismail
Copyright-Jahr
2019
Buch
The IoT Physical Layer

Print ISBN: 978-3-319-93099-2

Electronic ISBN: 978-3-319-93100-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-93100-5_18

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