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Article

Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals

by 1,2,*, 3 and 4
1
School of Electrical Engineering, Northwest Minzu University, Lanzhou 730030, China
2
Key Laboratory of China’s Ethnic Languages and Information Technology, Northwest Minzu University, Ministry of Education, Lanzhou 730030, China
3
New Energy (Photovoltaic) Industry Research Center, Qinghai University, Qinghai 810000, China
4
School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(1), 105; https://doi.org/10.3390/app9010105
Received: 3 November 2018 / Revised: 8 December 2018 / Accepted: 22 December 2018 / Published: 29 December 2018
Twelve-lead Electrocardiograph (ECG) signals fusion is crucial for further ECG signal processing. In this paper, based on the idea of the local weighted linear prediction algorithm, a novel fusion data algorithm is proposed, which was applied in data fusion of the 12-lead ECG signals. In order to analyze the signal quality comprehensively, the quality characteristics should be adequately retained in the final fused result. In our algorithm, the values for the weighted coefficient of state points were closely related to the final fused result. Thus, two fuzzy inference systems were designed to calculate the weighted coefficients. For the sake of assessing the performance of our method, synthetic ECG signals and realistic ECG signals were applied in the experiments. Experimental results indicate that our method can fuse the 12-lead ECG signals effectively with inherit the quality characteristics of original ECG signals inherited properly. View Full-Text
Keywords: ECG signal; quality assessment; state space reconstruction; local linear prediction; data fusion ECG signal; quality assessment; state space reconstruction; local linear prediction; data fusion
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MDPI and ACS Style

Diao, C.; Wang, B.; Cai, N. Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals. Appl. Sci. 2019, 9, 105. https://doi.org/10.3390/app9010105

AMA Style

Diao C, Wang B, Cai N. Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals. Applied Sciences. 2019; 9(1):105. https://doi.org/10.3390/app9010105

Chicago/Turabian Style

Diao, Chen, Bin Wang, and Ning Cai. 2019. "Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals" Applied Sciences 9, no. 1: 105. https://doi.org/10.3390/app9010105

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