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.
Highlights
ECG records the physiological information of cardiac activity by deploying electrodes placed at different positions of the body, which is widely applied in clinical diagnosis and monitoring
In [17], via the features of 12-lead ECG signals, Agrafioti et al addressed the identification of different human individuals, where the autocorrelation method and linear discriminant analysis were used to extract the features of the ECG segments from different leads
The outline of the rest of this paper is as follows: In Section 2, local weighted linear prediction algorithm (LWLPA) is briefly discussed as preliminary; Section 3 introduces novel data fusion algorithm (NDFA), based on LWLPA; the performance of NDFA is evaluated by synthetic ECG signals and realistic ECG signals in Section 4; and Section 5 contains the conclusion
Summary
ECG records the physiological information of cardiac activity by deploying electrodes placed at different positions of the body, which is widely applied in clinical diagnosis and monitoring. Studied a multi-level ECG quality assessment method based on a support vector machine. Thirteen signal quality indices were derived from the ECG signals, which were used for multi-level. In [17], via the features of 12-lead ECG signals, Agrafioti et al addressed the identification of different human individuals, where the autocorrelation method and linear discriminant analysis were used to extract the features of the ECG segments from different leads These features were combined further at the decision level by various voting principles. Inspired by LWLPA, in this paper, we propose a novel data fusion algorithm (NDFA) for 12-lead ECG signals, which can integrate the qualitative characteristics of. The outline of the rest of this paper is as follows: In Section 2, LWLPA is briefly discussed as preliminary; Section 3 introduces NDFA, based on LWLPA; the performance of NDFA is evaluated by synthetic ECG signals and realistic ECG signals in Section 4; and Section 5 contains the conclusion
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