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Published in: Journal of Medical Systems 7/2016

01-07-2016 | Mobile Systems

Evaluation of Digital Compressed Sensing for Real-Time Wireless ECG System with Bluetooth low Energy

Authors: Yishan Wang, Sammy Doleschel, Ralf Wunderlich, Stefan Heinen

Published in: Journal of Medical Systems | Issue 7/2016

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Abstract

In this paper, a wearable and wireless ECG system is firstly designed with Bluetooth Low Energy (BLE). It can detect 3-lead ECG signals and is completely wireless. Secondly the digital Compressed Sensing (CS) is implemented to increase the energy efficiency of wireless ECG sensor. Different sparsifying basis, various compression ratio (CR) and several reconstruction algorithms are simulated and discussed. Finally the reconstruction is done by the android application (App) on smartphone to display the signal in real time. The power efficiency is measured and compared with the system without CS. The optimum satisfying basis built by 3-level decomposed db4 wavelet coefficients, 1-bit Bernoulli random matrix and the most suitable reconstruction algorithm are selected by the simulations and applied on the sensor node and App. The signal is successfully reconstructed and displayed on the App of smartphone. Battery life of sensor node is extended from 55 h to 67 h. The presented wireless ECG system with CS can significantly extend the battery life by 22 %. With the compact characteristic and long term working time, the system provides a feasible solution for the long term homecare utilization.
Literature
1.
go back to reference Wang, Y., Yu, K., Wang, D., Zhao, C., Wang, L. and Wang, P., Multi-Model Diagnosis Method for Lung Cancer based on MOS-SAW Breath Detecting e-Nose, in Proceedings of The 14th International Symposium on Olfaction and Electronic Nose, 2011. Wang, Y., Yu, K., Wang, D., Zhao, C., Wang, L. and Wang, P., Multi-Model Diagnosis Method for Lung Cancer based on MOS-SAW Breath Detecting e-Nose, in Proceedings of The 14th International Symposium on Olfaction and Electronic Nose, 2011.
2.
go back to reference Wang, Y., Hu, Y., Wang, D., Yu, K., Wang, L., Zou, Y., Zhao, C., Zhang, X., Wang, P., and Ying, K., The analysis of volatile organic compounds biomarkers for lung cancer in exhaled breath, tissues and cell lines. Cancer Biomarkers 11:129–137, 2012.PubMed Wang, Y., Hu, Y., Wang, D., Yu, K., Wang, L., Zou, Y., Zhao, C., Zhang, X., Wang, P., and Ying, K., The analysis of volatile organic compounds biomarkers for lung cancer in exhaled breath, tissues and cell lines. Cancer Biomarkers 11:129–137, 2012.PubMed
3.
go back to reference Cao, H., Li, H., Stocco, L., and Leung, V. C. M., Wireless three-pad ECG system: challenges, design, and evaluations. Commun. Netw., J. 13:113–124, 2011.CrossRef Cao, H., Li, H., Stocco, L., and Leung, V. C. M., Wireless three-pad ECG system: challenges, design, and evaluations. Commun. Netw., J. 13:113–124, 2011.CrossRef
4.
go back to reference Rattfaelt, L., Bjoerefors, F., Nilsson, D., Wang, X., Norberg, P., and Ask, P., Properties of screen printed electrocardiography smartware electrodes investigated in an electro-chemical cell. Biomed. Eng. OnLine 12(64):1–11, 2013. Rattfaelt, L., Bjoerefors, F., Nilsson, D., Wang, X., Norberg, P., and Ask, P., Properties of screen printed electrocardiography smartware electrodes investigated in an electro-chemical cell. Biomed. Eng. OnLine 12(64):1–11, 2013.
5.
go back to reference Jeon, T., Kim, B., Jeon, M., and Lee, B.-G., Implementation of a portable device for real-time ECG signal analysis. Biomed. Eng. OnLine 13(160):1–13, 2014. Jeon, T., Kim, B., Jeon, M., and Lee, B.-G., Implementation of a portable device for real-time ECG signal analysis. Biomed. Eng. OnLine 13(160):1–13, 2014.
6.
go back to reference Havlik, J., Lhotska, L., Parak, J., Dvorak, J., Horcik, Z., and Pokorny, M., A modular system for rapid development of telemedical devices. J. Univ. Comput. Sci. 19(9):1242–1256, 2013. Havlik, J., Lhotska, L., Parak, J., Dvorak, J., Horcik, Z., and Pokorny, M., A modular system for rapid development of telemedical devices. J. Univ. Comput. Sci. 19(9):1242–1256, 2013.
7.
go back to reference Altini, M., Polito, S., Penders, J., Kim, H., Van Helleputte, N., Kim, S., Yazicioglu, F., An ECG patch combining a customized ultra-low-power ECG SoC with bluetooth low energy for long term ambulatory monitoring, In: Proceedings of the 2nd Conference on Wireless Health, pp. 1–2, 2011. Altini, M., Polito, S., Penders, J., Kim, H., Van Helleputte, N., Kim, S., Yazicioglu, F., An ECG patch combining a customized ultra-low-power ECG SoC with bluetooth low energy for long term ambulatory monitoring, In: Proceedings of the 2nd Conference on Wireless Health, pp. 1–2, 2011.
8.
go back to reference Munshi, M.C., Xu, X., Zou, X., Soetiono, E., Teo, C.S., Lian, Y., Wireless ECG plaster for body sensor network, In: Proceedings of ISSS-MDBS 2008, pp. 310–313, 2008. Munshi, M.C., Xu, X., Zou, X., Soetiono, E., Teo, C.S., Lian, Y., Wireless ECG plaster for body sensor network, In: Proceedings of ISSS-MDBS 2008, pp. 310–313, 2008.
9.
go back to reference Masse, F., Penders, J., Serteyn, A., Bussel, M. van and Arends, J., Miniaturized wireless ECG-monitor for real-time detection of epileptic seizures, In Proceedings of Wireless Health 2010, 2010. Masse, F., Penders, J., Serteyn, A., Bussel, M. van and Arends, J., Miniaturized wireless ECG-monitor for real-time detection of epileptic seizures, In Proceedings of Wireless Health 2010, 2010.
10.
go back to reference Chi, Y. M., Ng, P., and Cauwenberghs, G., Wireless noncontact ECG and EEG biopotential sensors. ACM Trans. Embed. Comput. Syst. 12(4):103–110, 2013.CrossRef Chi, Y. M., Ng, P., and Cauwenberghs, G., Wireless noncontact ECG and EEG biopotential sensors. ACM Trans. Embed. Comput. Syst. 12(4):103–110, 2013.CrossRef
11.
go back to reference Dixon, A. M. R., Allstot, E. G., Gangopadhyay, D., and Allstot, D. J., Compressed sensing system considerations for ECG and EMG wireless biosensors. Biomed. Circ. Syst. IEEE Trans. 6(2):156–166, 2012.CrossRef Dixon, A. M. R., Allstot, E. G., Gangopadhyay, D., and Allstot, D. J., Compressed sensing system considerations for ECG and EMG wireless biosensors. Biomed. Circ. Syst. IEEE Trans. 6(2):156–166, 2012.CrossRef
12.
go back to reference Candes, E. J., and Wakin, M. B., An introduction to compressive sampling. Signal Process. Mag. IEEE 25(2):21–30, 2008.CrossRef Candes, E. J., and Wakin, M. B., An introduction to compressive sampling. Signal Process. Mag. IEEE 25(2):21–30, 2008.CrossRef
13.
go back to reference Gangopadhyay, D., Allstot, E. G., Dixon, A. M. R., Natarajan, K., Gupta, S., and Allstot, D. J., Compressed sensing analog front-end for bio-sensor applications. Solid-State Circ., IEEE J. 49(2):426–438, 2014.CrossRef Gangopadhyay, D., Allstot, E. G., Dixon, A. M. R., Natarajan, K., Gupta, S., and Allstot, D. J., Compressed sensing analog front-end for bio-sensor applications. Solid-State Circ., IEEE J. 49(2):426–438, 2014.CrossRef
14.
go back to reference Allstot, E.G., Chen, A.Y., Dixon, A.M.R., Gangopadhyay, D., Mitsuda, H., Allstot, D.J., Compressed sensing of ECG bio-signals using one-bit measurement matrices, In: New Circuits and Systems Conference (NEWCAS), 2011 I.E. 9th International, pp. 213–216, 2011. Allstot, E.G., Chen, A.Y., Dixon, A.M.R., Gangopadhyay, D., Mitsuda, H., Allstot, D.J., Compressed sensing of ECG bio-signals using one-bit measurement matrices, In: New Circuits and Systems Conference (NEWCAS), 2011 I.E. 9th International, pp. 213–216, 2011.
15.
go back to reference Wang, Y., Wunderlich, R., Heinen, S., Design and evaluation of a novel wireless reconstructed 3-lead ECG monitoring system, In: Biomedical Circuits and Systems Conference (BioCAS), 2013 IEEE, pp. 362–365, 2013. Wang, Y., Wunderlich, R., Heinen, S., Design and evaluation of a novel wireless reconstructed 3-lead ECG monitoring system, In: Biomedical Circuits and Systems Conference (BioCAS), 2013 IEEE, pp. 362–365, 2013.
16.
go back to reference Wang, Y., Doleschel, S., Wunderlich, R., and Heinen, S., A wearable wireless ECG monitoring system with dynamic transmission power control for long-term homecare. J. Med. Syst. 39(3):1–10, 2015.CrossRef Wang, Y., Doleschel, S., Wunderlich, R., and Heinen, S., A wearable wireless ECG monitoring system with dynamic transmission power control for long-term homecare. J. Med. Syst. 39(3):1–10, 2015.CrossRef
17.
go back to reference Wang., Y., Wunderlich., R. and Heinen., S., A low noise wearable wireless ECG system with body motion cancellation for long term homecare, In Proceedings of IEEE Healthcom 2013 Conference, 2013. Wang., Y., Wunderlich., R. and Heinen., S., A low noise wearable wireless ECG system with body motion cancellation for long term homecare, In Proceedings of IEEE Healthcom 2013 Conference, 2013.
18.
go back to reference Candes, E. J., Romberg, J. K., and Tao, T., Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8):1207–1223, 2006.CrossRef Candes, E. J., Romberg, J. K., and Tao, T., Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8):1207–1223, 2006.CrossRef
19.
go back to reference Donoho, D. L., Compressed sensing. IEEE Trans. Inf. Theory 52(4):1289–1306, 2006.CrossRef Donoho, D. L., Compressed sensing. IEEE Trans. Inf. Theory 52(4):1289–1306, 2006.CrossRef
20.
go back to reference Zou, W., and Pan, X., Compressed-sensing-based fluorescence molecular tomographic image reconstruction with grouped sources. Biomed. Eng. OnLine 13(119):1–15, 2014. Zou, W., and Pan, X., Compressed-sensing-based fluorescence molecular tomographic image reconstruction with grouped sources. Biomed. Eng. OnLine 13(119):1–15, 2014.
21.
go back to reference Mallat, S., A wavelet tour of signal processing: the sparse way. Academic Press, Boston, 2009. Mallat, S., A wavelet tour of signal processing: the sparse way. Academic Press, Boston, 2009.
22.
go back to reference Baraniuk, R. G., Cevher, V., Duarte, M. F., and Hegde, C., Model-based compressive sensing. IEEE Trans. Inf. Theory 56(4):1982–2001, 2010.CrossRef Baraniuk, R. G., Cevher, V., Duarte, M. F., and Hegde, C., Model-based compressive sensing. IEEE Trans. Inf. Theory 56(4):1982–2001, 2010.CrossRef
23.
go back to reference Mallat, S. G., and Zhang, Z., Matching pursuits with time-frequency dictionaries. Signal Process. IEEE Trans. 41(12):3397–3415, 1993.CrossRef Mallat, S. G., and Zhang, Z., Matching pursuits with time-frequency dictionaries. Signal Process. IEEE Trans. 41(12):3397–3415, 1993.CrossRef
24.
go back to reference Pope, G., Compressive sensing: A summary of reconstruction algorithms, Master’s thesis, Eidgenössische Technische Hochschule, Zürich, Department of Computer Science, 2009. Pope, G., Compressive sensing: A summary of reconstruction algorithms, Master’s thesis, Eidgenössische Technische Hochschule, Zürich, Department of Computer Science, 2009.
25.
go back to reference Tropp, J. A., and Gilbert, A. C., Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12):4655–4666, 2007.CrossRef Tropp, J. A., and Gilbert, A. C., Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12):4655–4666, 2007.CrossRef
26.
go back to reference Needell, D., and Tropp, J. A., Cosamp: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26(3):301–321, 2009.CrossRef Needell, D., and Tropp, J. A., Cosamp: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26(3):301–321, 2009.CrossRef
27.
go back to reference Blumensath, T., and Davies, M. E., Iterative hard thresholding for compressed sensing. Appl. Comput. Harmon. Anal. 27(3):265–274, 2009.CrossRef Blumensath, T., and Davies, M. E., Iterative hard thresholding for compressed sensing. Appl. Comput. Harmon. Anal. 27(3):265–274, 2009.CrossRef
28.
go back to reference Blumensath, T., Accelerated iterative hard thresholding. Signal Process. 92(3):752–756, 2012.CrossRef Blumensath, T., Accelerated iterative hard thresholding. Signal Process. 92(3):752–756, 2012.CrossRef
29.
go back to reference van den Berg, E., Convex optimization for generalized sparse recovery, PhD thesis, The University of British Columbia, Department of Computer Science, 2009. van den Berg, E., Convex optimization for generalized sparse recovery, PhD thesis, The University of British Columbia, Department of Computer Science, 2009.
30.
go back to reference Li, X., and Luo, S., A compressed sensing-based iterative algorithm for ct reconstruction and its possible application to phase contrast imaging. Biomed. Eng. OnLine 10(73):1–14, 2011.CrossRef Li, X., and Luo, S., A compressed sensing-based iterative algorithm for ct reconstruction and its possible application to phase contrast imaging. Biomed. Eng. OnLine 10(73):1–14, 2011.CrossRef
31.
go back to reference Burns, A., Doheny, E.P., Greene, B.R., Foran, T., Leahy, D., O’Donovan, K., McGrath, M.J., ShimmerTM: an extensible platform for physiological signal capture, In: Proceedings of Annual International Conference of IEEE EMBC 2010, pp. 3759–3762, 2010. Burns, A., Doheny, E.P., Greene, B.R., Foran, T., Leahy, D., O’Donovan, K., McGrath, M.J., ShimmerTM: an extensible platform for physiological signal capture, In: Proceedings of Annual International Conference of IEEE EMBC 2010, pp. 3759–3762, 2010.
32.
go back to reference Gaxiola-Sosa, J.E., Mohsin, N., Palliyali, A.J., Tafreshi, R., Entesari, K., A portable 12-lead ECG wireless medical system for continuous cardiac-activity monitoring, In: Proceedings of MECBME 2014, pp. 123–126, 2014. Gaxiola-Sosa, J.E., Mohsin, N., Palliyali, A.J., Tafreshi, R., Entesari, K., A portable 12-lead ECG wireless medical system for continuous cardiac-activity monitoring, In: Proceedings of MECBME 2014, pp. 123–126, 2014.
33.
go back to reference Gao, H., Duan, X., Guo, X., Huang, A., Jiao, B., Design and tests of a smartphones-based multi-lead ECG monitoring system, In: Proceedings of Annual International Conference of IEEE EMBC 2013, pp. 2267–2270, 2013. Gao, H., Duan, X., Guo, X., Huang, A., Jiao, B., Design and tests of a smartphones-based multi-lead ECG monitoring system, In: Proceedings of Annual International Conference of IEEE EMBC 2013, pp. 2267–2270, 2013.
34.
go back to reference Tan, T.-H., Chang, C.-S., Huang, Y.-F., Chen, Y.-F., and Lee, C., Development of a portable linux-based ECG measurement and monitoring system. J. Med. Syst. 35(4):559–569, 2011.CrossRefPubMed Tan, T.-H., Chang, C.-S., Huang, Y.-F., Chen, Y.-F., and Lee, C., Development of a portable linux-based ECG measurement and monitoring system. J. Med. Syst. 35(4):559–569, 2011.CrossRefPubMed
35.
go back to reference Winokur, E. S., Delano, M. K., and Sodini, C. G., A wearable cardiac monitor for long-term data acquisition and analysis. IEEE Trans. Biomed. Eng. 60(1):189–192, 2013.CrossRefPubMed Winokur, E. S., Delano, M. K., and Sodini, C. G., A wearable cardiac monitor for long-term data acquisition and analysis. IEEE Trans. Biomed. Eng. 60(1):189–192, 2013.CrossRefPubMed
36.
go back to reference Gomez-Clapers, J., and Casanella, R., A fast and easy-to-use ECG acquisition and heart rate monitoring system using a wireless steering wheel. Sensors J. IEEE 12(3):610–616, 2012.CrossRef Gomez-Clapers, J., and Casanella, R., A fast and easy-to-use ECG acquisition and heart rate monitoring system using a wireless steering wheel. Sensors J. IEEE 12(3):610–616, 2012.CrossRef
37.
go back to reference Fensli, R., Dale, J., O’Reilly, P., O’Donoghue, J., Sammon, D., and Gundersen, T., Towards improved healthcare performance: examining technological possibilities and patient satisfaction with wireless body area networks. J. Med. Syst. 34(4):767–775, 2010.CrossRefPubMed Fensli, R., Dale, J., O’Reilly, P., O’Donoghue, J., Sammon, D., and Gundersen, T., Towards improved healthcare performance: examining technological possibilities and patient satisfaction with wireless body area networks. J. Med. Syst. 34(4):767–775, 2010.CrossRefPubMed
38.
go back to reference Polania, L. F., Carrillo, R. E., Blanco-Velasco, M., and Barner, K. E., Exploiting prior knowledge in compressed sensing wireless ECG systems. Biomed. Health Inf. IEEE J. 19(2):508–519, 2015.CrossRef Polania, L. F., Carrillo, R. E., Blanco-Velasco, M., and Barner, K. E., Exploiting prior knowledge in compressed sensing wireless ECG systems. Biomed. Health Inf. IEEE J. 19(2):508–519, 2015.CrossRef
39.
go back to reference Mamaghanian, H., Khaled, N., Atienza, D., and Vandergheynst, P., Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. Biomed. Eng. IEEE Trans. 58(9):2456–2466, 2011.CrossRef Mamaghanian, H., Khaled, N., Atienza, D., and Vandergheynst, P., Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. Biomed. Eng. IEEE Trans. 58(9):2456–2466, 2011.CrossRef
40.
go back to reference Liu, B., Zhang, Z., Xu, G., Fan, H., and Fu, Q., Energy efficient telemonitoring of physiological signals via compressed sensing: A fast algorithm and power consumption evaluation. Biomed. Signal Process. Control 11:80–88, 2014.CrossRef Liu, B., Zhang, Z., Xu, G., Fan, H., and Fu, Q., Energy efficient telemonitoring of physiological signals via compressed sensing: A fast algorithm and power consumption evaluation. Biomed. Signal Process. Control 11:80–88, 2014.CrossRef
41.
go back to reference Fauvel, S., and Ward, R. K., An energy efficient compressed sensing framework for the compression of electroencephalogram signals. Sensors 14(1):1474–1496, 2014.CrossRefPubMedPubMedCentral Fauvel, S., and Ward, R. K., An energy efficient compressed sensing framework for the compression of electroencephalogram signals. Sensors 14(1):1474–1496, 2014.CrossRefPubMedPubMedCentral
42.
go back to reference Zhang, Z., Jung, T.-P., Makeig, S., and Rao, B. D., Compressed sensing for energy-efficient wireless Telemonitoring of noninvasive fetal ECG via block sparse bayesian learning. Biomed. Eng. IEEE Trans. 60(2):300–309, 2013.CrossRef Zhang, Z., Jung, T.-P., Makeig, S., and Rao, B. D., Compressed sensing for energy-efficient wireless Telemonitoring of noninvasive fetal ECG via block sparse bayesian learning. Biomed. Eng. IEEE Trans. 60(2):300–309, 2013.CrossRef
43.
go back to reference Pant, J. K., and Krishnan, S., Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning. Biomed. Circ. Syst. IEEE Trans. 8(2):293–302, 2014.CrossRef Pant, J. K., and Krishnan, S., Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning. Biomed. Circ. Syst. IEEE Trans. 8(2):293–302, 2014.CrossRef
44.
go back to reference Cho, G. Y., Lee, S. J., and Lee, T. R., An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems. J. Med. Syst. 39(161):1–8, 2015. Cho, G. Y., Lee, S. J., and Lee, T. R., An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems. J. Med. Syst. 39(161):1–8, 2015.
Metadata
Title
Evaluation of Digital Compressed Sensing for Real-Time Wireless ECG System with Bluetooth low Energy
Authors
Yishan Wang
Sammy Doleschel
Ralf Wunderlich
Stefan Heinen
Publication date
01-07-2016
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 7/2016
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-016-0526-1

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