Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: ECG Monitoring and Analysis · Fault Detection and Control Systems · EEG and Brain-Computer Interfaces
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical Engineering, Northwest Minzu University, Lanzhou 730030, China
Key Laboratory of China’s Ethnic Languages and Information Technology, Northwest Minzu University, Ministry of Education, Lanzhou 730030, China
New Energy (Photovoltaic) Industry Research Center, Qinghai University, Qinghai 810000, China
School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Gansu Province · Fundamental Research Funds for the Central Universities
References (37)
Show source-provided references
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