Abstract
The chapter discusses the performance of the presented system. The high-level simulation results are presented, and then a detailed comparison with the published work is given. The chapter is concluded by presenting the obtained results from the first chip tapeout of the introduced system.
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A.L. Goldberger, L.A. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
P.S. Hamilton, W.J. Tompkins, Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng. 33(12), 1157–1165 (1986)
J.P. MartÃnez, R. Almeida, S. Olmos, A.P. Rocha, P. Laguna, A wavelet-based ECG delineator: Evaluation on standard databases. IEEE Trans. Biomed. Eng. 51(4), 570–581 (2004)
A. Cost, G.G. Cano, QRS detection based on hidden markov modeling, in Engineering in Medicine and Biology Society, 1989. Images of the Twenty-First Century., Proceedings of the Annual International Conference of the IEEE Engineering in (IEEE, 1989), p. 34–35
E.B. Mazomenos, D. Biswas, A. Acharyya, T. Chen, K. Maharatna, J. Rosengarten, J. Morgan, N. Curzen, A low-complexity ECG feature extraction algorithm for mobile healthcare applications. IEEE J. Biomed. Health Inform. 17(2), 459–469 (2013)
E. Mazomenos, T. Chen, A. Acharyya, A. Bhattacharya, J. Rosengarten, K. Maharatna, A time-domain morphology and gradient based algorithm for ECG feature extraction, in Industrial Technology (ICIT), 2012 IEEE International Conference on (IEEE, 2012), p. 117–122
R.V. Andreão, B. Dorizzi, J. Boudy, ECG signal analysis through hidden Markov models. IEEE Trans. Biomed. Eng. 53(8), 1541–1549 (2006)
Y. Sun, K.L. Chan, S.M. Krishnan, Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovasc. Disord. 5(1), 28 (2005)
P. Laguna, R.G. Mark, A. Goldberg, G.B. Moody, A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG, in Computers in Cardiology 1997 (IEEE, Piscataway, 1997), pp. 673–676
P. De Chazal, M. O’Dwyer, R.B. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206 (2004)
A. Amann, R. Tratnig, K. Unterkofler, Detecting ventricular fibrillation by time-delay methods. IEEE Trans. Biomed. Eng. 54(1), 174–177 (2007)
F. Alonso-Atienza, E. Morgado, L. Fernandez-Martinez, A. GarcÃa-Alberola, J. Rojo-Alvarez, Detection of life-threatening arrhythmias using feature selection and support vector machines. I.E.E.E. Trans. Biomed. Eng. 61(3), 832–840 (2014)
Q. Li, C. Rajagopalan, G. Clifford, Ventricular fibrillation and tachycardia classification using machine learning method. I.E.E.E. Trans. Biomed. Eng. 61(6), 1607–1613 (2013)
O. Sayadi, M.B. Shamsollahi, G.D. Clifford, Robust detection of premature ventricular contractions using a wave-based bayesian framework. IEEE Trans. Biomed. Eng. 57(2), 353–362 (2010)
S. Lee, J. Hong, K. Lin, C. Hsieh, M. Liang, S. Chien, Low-power wireless ECG acquisition and classification system for body sensor networks. IEEE J. Biomed. Health Inform. 19(1), 236–246 (2015)
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)
H. Kim, R.F. Yazicioglu, T. Torfs, P. Merken, H.-J. Yoo, C. Van Hoof, A low power ECG signal processor for ambulatory arrhythmia monitoring system, in VLSI Circuits (VLSIC), 2010 IEEE Symposium on (IEEE, 2010), p. 19–20
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Saleh, H., Bayasi, N., Mohammad, B., Ismail, M. (2018). Performance and Results. In: Self-powered SoC Platform for Analysis and Prediction of Cardiac Arrhythmias . Analog Circuits and Signal Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-63973-4_5
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DOI: https://doi.org/10.1007/978-3-319-63973-4_5
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