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- W2947372263 abstract "In this paper, we present a filter for de-noising an ECG mixed EEG signal where ECG considered as an artifact and the aim is to remove that ECG signal from the noisy EEG (EEG+ECG) signal. The input to the system is the estimated ECG signal which works on noise cancellation technology to retrieve the original EEG signal. We have taken a one-layered feed forward network known as Functional Link Adaptive Neural Network (FLANN) and a three layered Radial Basis Functional Network (RBFN) for the filter design. For weight updating there are three adaptive algorithms are applied like Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Filtered Least Mean Square (FxLMS). The results of three algorithms are analyzed." @default.
- W2947372263 created "2019-06-07" @default.
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- W2947372263 date "2018-12-01" @default.
- W2947372263 modified "2023-09-23" @default.
- W2947372263 title "ECG Artifact Removal of EEG signal using Adaptive Neural Network" @default.
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- W2947372263 doi "https://doi.org/10.1109/iciinfs.2018.8721423" @default.
- W2947372263 hasPublicationYear "2018" @default.
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