Implementing a smart method to eliminate artifacts of vital signals

Background: Electroencephalography (EEG) has vital and significant applications in different medical fields and is used for the primary evaluation of neurological disorders. Hence, having easy access to suitable and useful signal is very important. Artifacts are undesirable confusions which are generally originated from inevitable human activities such as heartbeat, blinking of eyes and facial muscle activities while receiving EEG signal. It can bring about deformation in these waves though. Objective: The objective of this study was to find a suitable solution to eliminate the artifacts of Vital Signals. Methods: In this study, wavelet transform technique was used. This method is compared with threshold level. The threshold intensity is efficiently crucial because it should not remove the original signal instead of artifacts, and does not hold artifact signal instead of original ones. In this project, we seek to find and implement the algorithm with the ability to automatically remove the artifacts in EEG signals. For this purpose, the use of adaptive filtering methods such as wavelet analysis is appropriate. Finally, we observed that Functional Link Neural Network (FLN) performance is better than ANFIS and RBFN to remove such artifacts. Results: We offer an intelligent method for removing artifacts from vital signals in neurological disorders. Conclusion: The proposed method can obtain more accurate results by removing artifacts of vital signals and can be useful in the early diagnosis of neurological and cardiovascular disorders.