Abstract
The medical domain is one of the most rapidly expanding application areas of Internet of Things (IoT) technology. For chronic diseases, this technology can be highly useful for the patient, providing constant monitoring and ability for timely intervention of medical staff in case of an emergency. This intended system behavior imposes new requirements to the design and implementation of processing flows implemented on embedded IoT devices which are already constrained by limited computational capabilities and power budget. This work aims at designing and implementing such a bio-medical signal analysis flow based on the case study of arrhythmia detection using electrocardiogram signals and machine learning techniques. Different architectural decisions of the flow are explored at high level and the final optimized version is implemented on a state-of-the-art IoT node. The evaluation of the execution flow on this device provides information on the actual requirements of each sub-component of the flow combined with an analysis of its behavior as computational requirements of the machine learning algorithms scale up.
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Electrocardiography, https://en.wikipedia.org/wiki/Electrocardiography
F.I. Marcus, J.N. Ruskin, B. Surawicz, Arrhythmias. J. Am. Coll. Cardiol. 10 (2), 66A–72A (1987)
E.D. Übeyli, ECG beats classification using multiclass support vector machines with error correcting output codes. Digital Signal Process. 17 (3), 675–684 (2007)
R. Mark, G. Moody, MIT-BIH Arrhythmia Database Directory (Massachusetts Institute of Technology, Cambridge, 1988). Available online from: http://www.physionet.org/physiobank/database/mitdb/
R.J. Martis, U.R. Acharya, L.C. Min, ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed. Signal Process. Control 8 (5), 437–448 (2013)
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)
M.U. Guide, The Mathworks, Inc., vol. 5, p. 333, Natick, MA (1998)
Ojha, D. K., & Subashini, M. (2014). Analysis of Electrocardiograph (ECG) Signal for the Detection of Abnormalities Using MATLAB. World Acad. Sci. Eng. Technol. Int. J. Med. Health Pharm. Biomed. 8 (2), 114–117.
W. Zong, G. Moody, Wqrs-single-channel QRS detector based on length transform. Physionet (2003). http://wwwphysionetorg/physiotools/wag/wqrs-1htm
P. Laguna, R. Jan, E. Bogatell, D. Anglada, QRS detection and waveform boundary recognition using ecgpuwave. PhysioToolkit Open Source Software for Biomedical Science and Engineering, in Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 50–53 (2002)
P. Laguna, R. Jané, P. Caminal, Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Comput. Biomed. Res. 27 (1), 45–60 (1994)
R. Jané, A. Blasi, J. García, P. Laguna, Evaluation of an automatic threshold based detector of waveform limits in Holter ECG with the qt database. Comput. Cardiol. 24, 295–298 (1997)
I. Daubechies, The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inform. Theory 36 (5), 961–1005 (1990)
I. Daubechies et al., Ten Lectures on Wavelets, vol. 61 (SIAM, Philadelphia, 1992)
C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20 (3), 273–297 (1995)
C.-C. Chang, C.-J. Lin, Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2 (3), 27 (2011)
Tsoutsouras, V., Azariadi, D., Xydis, S., & Soudris, D. (2015, December). Effective Learning and Filtering of Faulty Heart-Beats for Advanced ECG Arrhythmia Detection using MIT-BIH database. In Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 50–53
M.C. Ramon, Intel Galileo and Intel Galileo Gen 2 (Springer, New York, 2014)
G.D. Clifford, F. Azuaje, P. McSharry, Advanced Methods and Tools for ECG Data Analysis (Artech House Publishing, London, 2006)
G.B. Moody, R.G. Mark, The impact of the mit-bih arrhythmia database. IEEE Eng. Med. Biol. Mag. 20 (3), 45–50 (2001).
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, 1196–1206 (2004)
P. de Chazal, R. Reilly, A patient-adapting heartbeat classifier using ecg morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 53 (12), 2535–2543 (2006)
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Tsoutsouras, V., Azariadi, D., Koliogewrgi, K., Xydis, S., Soudris, D. (2017). Software Design and Optimization of ECG Signal Analysis and Diagnosis for Embedded IoT Devices. In: Keramidas, G., Voros, N., Hübner, M. (eds) Components and Services for IoT Platforms. Springer, Cham. https://doi.org/10.1007/978-3-319-42304-3_15
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DOI: https://doi.org/10.1007/978-3-319-42304-3_15
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