Abstract
This paper presents a fast algorithm for the aggregation and analysis of ECG data. The whole process of fusion and analysis can be divided into three stages. ECG signal de-noising is the first stage. A combined filter is used to cut out the noises from ECG signals. In the second stage, a simple method named SDTW (the Sample Dynamic Time Wrapping) is proposed to improve the time efficiency of DTW. Then SDTW and K-means algorithm are applied to attain templates as well as compress templates. The last stage is to train a BP neural network with the compressed templates and other ECG features. Experiments with the MIT-BIH arrhythmia database shows that our algorithm can efficiently improve the recognition accuracy and shorten the recognition time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sadhukhan, D., Mitra, M.: ECG noise reduction using fourier coefficient suppression. In: International Conference on Control, Instrumentation Energy and Communication, pp. 142–146 (2014)
Wei, A.Y.: ECG de-noising based on MP algorithm. Appl. Mech. Mater. 433–435, 510–513 (2013)
Samadi, S., Shamsollahi, M.B.: ECG noise reduction using empirical mode decomposition based on combination of instantaneous half period and soft-thresholding. In: The Proceedings of Middle East Conference on Biomedical Engineering (MECBME), Doha, Qatar, pp. 244–248 (2014)
Mallick, P.K.: Baseline Drift Removal of ECG Signal: Comparative Analysis of Filtering Techniques. Information Resources Management Association (IRMA) (2016)
Cuomo, S., De Pietro, G., Farina, R., Galletti, A., Sannino, G.: A novel O(n) numerical scheme for ECG signal denoising. Procedia Comput. Sci. 51(1), 775–784 (2015)
Shaharm, M., Nayebi, K.: Classification of multichannel ECG signals using a cross一distance analysis. In: IEEE 23rd Annual International Conference of Engineering in Medicine & Biology Society (EMBC), Istanbul, Turkey, pp. 2182–2185 (2001)
Polat, K., Akdemir, B., Gunes, S.: Computer aided diagnosis of ECG data on the least square support vector machine. Digit. Sig. Proc. 18(1), 25–32 (2008)
Exarchos, T.P., Tsipouras, M.G., Exarchos, C.P., et al.: A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artif. Intell. Med. 40(3), 187–200 (2007). Elsevier
Saini, I., Singh, D., Khosla, A.: Delineation of ECG wave components using K-Nearest Neighbor (KNN) algorithm: ECG wave delineation using KNN. In: 2013 Tenth International Conference on Information Technology: New Generations (ITNG), vol. 25(1), pp. 712–717, 15–17 2013
Shadmand, S., Mashoufi, B.: Personalized ECG signal classification using block-based neural-network and particle swarm optimization. In: International Conference of the IEEE Engineering in Medicine & Biology Society, vol. 3(37), pp. 2182–2185 (2001)
Acknowledgements
The research work was supported by National Natural Science Foundation of China (U1433116) and the Aviation Science Foundation of China (20145752033).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Zhang, M., Pi, D. (2016). Data Aggregation and Analysis: A Fast Algorithm of ECG Recognition Based on Pattern Matching. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-48674-1_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48673-4
Online ISBN: 978-3-319-48674-1
eBook Packages: Computer ScienceComputer Science (R0)