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Implementing a Smart Method to Eliminate Artifacts of Vital Signals
1Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Journal of Biomedical Physics & Engineering
|December 22, 2015
Summary
This study presents an intelligent method to automatically remove artifacts from electroencephalography (EEG) signals, improving diagnostic accuracy for neurological and cardiovascular disorders.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Signal Processing
Background:
- Electroencephalography (EEG) is crucial for evaluating neurological disorders.
- Artifacts from human activities can distort EEG signals.
- Accurate EEG signals are vital for reliable medical evaluation.
Purpose of the Study:
- To develop an effective solution for eliminating artifacts in vital signals.
- To implement an algorithm for automatic artifact removal from EEG data.
Main Methods:
- Wavelet transform technique was employed for artifact removal.
- Adaptive filtering methods, including wavelet analysis, were utilized.
- Functional Link Neural Network (FLN) performance was compared to ANFIS and RBFN.
Main Results:
- An intelligent method for artifact removal from vital signals was developed.
- The Functional Link Neural Network (FLN) demonstrated superior performance in artifact removal compared to other methods.
Conclusions:
- The proposed method accurately removes artifacts from vital signals.
- This technique aids in the early diagnosis of neurological and cardiovascular disorders.
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