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Analysis of electrocardiogram and heart rate provides useful information about health condition of a patient. The North Sea Bicycle Race is an annual cycling competition in Norway. Examination of ECG recordings collected from participants of this race may allow defining and evaluating the relationship between physical endurance exercises and heart electrophysiology. Parameters reflecting potentially alarming deviations are to be identified in this study. This paper presents results of a time-domain analysis of ECG data collected in 2014, implementing K-Means clustering. A double stage analysis strategy, aimed at producing hierarchical clusters, is proposed. The first phase allows rough separation of data. Second stage is applied to reveal internal structure of the majority clusters. In both steps, discrepancies driving the separation could stem from three sources. Firstly, they could be signs of abnormalities in electrical activity of the heart. Secondly, they may allow discriminating between natural groups of participants – according to sex, age, physical fitness. Finally, some deviations could result from faults in data extraction, therefore serving in evaluation of the parameters. The clusters were defined predominantly by combinations of features: heartbeat signals correlation, P-wave shape, and RR intervals; none of the features alone was discriminative for all the clusters.
Rocznik
Tom
Strony
23--32
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
autor
- Łódź University of Technology, Institute of Electronics, ul. Wólczańska 211/215, 90- 924 Łódź, Poland
autor
- University of Stavanger, Faculty of Science and Technology, Department of Electrical and Computer Engineering, 4036 Stavanger, Norway
autor
- Łódź University of Technology, Institute of Electronics, ul. Wólczańska 211/215, 90- 924 Łódź, Poland
autor
- University of Stavanger, Faculty of Science and Technology, Department of Electrical and Computer Engineering, 4036 Stavanger, Norway
autor
- University of Stavanger, Faculty of Science and Technology, Department of Electrical and Computer Engineering, 4036 Stavanger, Norway
Bibliografia
- [1] X. Dong, C. Wang, W. Si, “ECG beat classification via deterministic learning”, Neurocomputing,vol. 240, May 2017, 1–12. DOI: 10.1016/j.neucom.2017.02.056.
- [2] F. Castells, P. Laguna, L. Sornmo, A. Bollmann, J. Roig, “Principal component analysis in ECGsignal processing”, EURASIP J. Adv. Signal Process.,2007. DOI: 10.1155/2007/74580.
- [3] A. Daamouche, L. Hamami, N. Alajlan, F. Melgani, “A wavelet optimization approach for ECG signal classification”, Biomed. Signal Process. Control,vol. 7, 342–349, Jul. 2012. DOI: 10.1016/j.bspc.2011.07.001.
- [4] D. Benitez, P. Gaydecki, A. Zaidi, A. P. Fitzpatrick,“The use of the Hilbert transform in ECGsignal analysis”, Comput. Biol. Med., vol. 31, no. 5, 399–406, 2001. DOI: 10.1016/S0010-4825(01)00009-9.
- [5] M. Moavenian, H. Khorrami, “A qualitative comparison of Artificial Neural Networks and SupportVector Machines in ECG arrhythmias classification”, EXPERT Syst. Appl., vol. 37, no. 4, Apr. 2010,3088–3093. DOI: 10.1016/j.eswa.2009.09.021.
- [6] M. M. A. Rahhal, Y. Bazi, H. AlHichri, N. Alajlan,F. Melgani, R. Yager, “Deep learning approach for active classification of electrocardiogram signals”, Inf. Sci., vol. 345, Jun. 2016, 340–354. DOI: 10.1016/j.ins.2016.01.082.
- [7] M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt, and L. Sornmo, “Clustering ECG complexes using Hermite functions and self-organizing maps”, IEEE Trans. Biomed. Eng., vol. 47, no. 7, 838–848, Jul. 2000. DOI: 10.1109/10.846677.
- [8] “biosppy.signals — BioSPPy 0.2.2 documentation” [Online] Available: http://biosppy.readthedocs. io/en/stable/biosppy.signals.html#biosppy-signals-ecg. [Accessed: 16-Jul-2016].
- [9] “Documentation — SciPy.org” [Online]. Available: https://www.scipy.org/docs.html. [Accessed: 29-Apr-2017].
- [10] “scikit-learn: machine learning in Python —scikit-learn 0.18.1 documentation” [Online]. Available: http://scikit-learn.org/stable/. [Accessed: 29-Apr-2017].
- [11] F. Pedregosa et al., “Scikit-learn: Machine learning in Python”, J. Mach. Learn. Res., vol. 12, Oct. 2011, 2825–2830. DOI: 10.1016/j.patcog.2011.04.006.
- [12] A. Lourenço, H. Silva, P. Leite, R. Lourenco, A. Fred, “Real Time Electrocardiogram Segmentation for Finger based ECG Biometrics (PDF) – Semantic Scholar”. [Online]. Available: https://www.semanticscholar. org/paper/Real-Time-Electrocardiogram-Segmentation-for-Louren%C3%A7o-Silva/358eee4f2080303f1ad0c7df866b98fb89222d8d/pdf. [Accessed: 13-Aug-2016].
- [13] I. I. Christov, “Real time electrocardiogram QRS detection using combined adaptive threshold”, Biomed. Eng. OnLine, vol. 3, 2004, p. 28. DOI: 10.1186/1475-925X-3-28.
- [14] A. Gautam, Y. D. Lee, W. Y. Chung, “ECG Signal De-noising with Signal Averaging and Filtering Algorithm”. In: Third International Conference on Convergence and Hybrid Information Technology, 2008, vol. 1, 409–415. DOI: 10.1109/ICCIT. 2008.393.
- [15] P. Laguna, R. Jané, P. Caminal, “Automatic detection of wave boundaries in multilead ECG signals: Validation with the CSE database”, Comput. Biomed. Res., vol. 27, no. 1, 1994, 45–60. DOI:10.1006/cbmr.1994.1006.
- [16] P. W. Macfarlane, B. Devine, E. Clark, “The university of Glasgow (Uni-G) ECG analysis program”, Computers in Cardiology, 2005, Lyon, 2005, 451–454. DOI: 10.1109/CIC.2005.1588134.
- [17] “Glasgow 12-lead Analysis Program – Physician’s Guide”, Physio Control. [Online]. Available: https://docs.google.com/viewerng/viewer?url=http://www.physio-control.com/uploadedFiles/learning/clinical-topics/Glasgow_PhysiciansGuide.pdf.[Accessed: 21-Jul-2016].
- [18] K. Wang, R. W. Asinger, H. J. Marriott, “ST-segment elevation in conditions other than acute myocardial infarction”, N. Engl. J. Med., vol. 349, no. 22, 2003, 2128–2135. DOI: 10.1056/NEJMra022580.
- [19] U. Demšar, P. Harris, C. Brunsdon, A. S. Fotheringham, and S. McLoone, “Principal Component Analysis on Spatial Data: An Overview”, Ann. Assoc. Am. Geogr., vol. 103, no. 1, 106–128, Jan. 2013. DOI: 10.1080/00045608.2012.689236.
- [20] B. Hariharan, J. Malik, and D. Ramanan, “Discriminative Decorrelation for Clustering and Classification”, in Computer Vision – ECCV 2012, 2012, 459–472. DOI: 10.1007/978-3-642-33765-9_33
- [21] P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis”, J. Comput. Appl. Math., vol. 20, 53–65, 1987. DOI: 10.1016/0377-0427(87)90125-7.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-e55b728e-8398-4c0a-92a4-49a38bbcc0c4