BACKGROUND: Traditional least mean square algorithm (LMS) tends to converge faster and thus the larger the steady-state error of the algorithm. OBJECTIVE: In order to solve this issue, an improved adaptive normalized least mean square (NLMS) ECG signal denoising algorithm is proposed through utilizing the NLMS and the least mean square algorithm with added momentum term (MLMS). METHODS: The algorithm firstly performs LMS adaptive filtering on the original ECG signal. Then, the algorithm uses the relative error of the prior error signal and the posterior error signal before and after filtering to adaptively determine the iteration step factor. Finally, the expected error is set to determine whether the denoising meets the expected requirements. This method is applied to the MIT-BIH ECG database established by the Massachusetts Institute of Technology. RESULTS: Experimental results have shown that the proposed algorithm can achieve good denoising for the target signal, and the average signal to noise ratio (SNR) of the proposed method is 17.6016, the RMSE is only 0.0334, and the average smoothness index R is only 0.0325. CONCLUSION: The proposed algorithm effectively removes the original ECG signal noise, and improves the smoothness of the signal the denoising efficiency.
In order to solve the issue that the traditional k-means algorithm falls into the local optimal solution in video summarization due to unreasonable initial parameter setting, a video summarization generation algorithm by using improved clustering and silhouette coefficient was proposed. Firstly, color features and texture features are extracted and fused from the decomposed video frames. Secondly, the hierarchical clustering algorithm is used to obtain the initial clustering results. And then, the improved k-means algorithm with silhouette coefficient is introduced to optimize the initial clustering results. Finally, the nearest frame from the cluster center is selected as the keyframe, and all the final keyframes are arranged in the order of the time sequence in the original video to constitute video summarization. The proposed algorithm is evaluated on the benchmark Open Video Database dataset with an average 71% precision, 84% recall rate, and 76% F-score, which is higher than state-of-the-art video summarization methods. Moreover, it generates video keyframes that are closer to user summaries, and it improves effectively the overall quality of the generated summary.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.