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.
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Acknowledgements
The research work was supported by National Natural Science Foundation of China (U1433116) and the Aviation Science Foundation of China (20145752033).
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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
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DOI: https://doi.org/10.1007/978-3-319-48674-1_28
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