Abstract
The problem of noise interference in ECG signals has been addressed in this paper. Specifically, a method has been developed to filter out Electromyography noise (EMG) from ECG signals. A dataset of ECG signals with varying levels of EMG noise has been collected using the MIT-BIH dataset. An algorithm has been designed and implemented using the DA FIR filter coupled with Kaiser windowing technique to filter out the noise. The algorithm has been tested on the collected dataset using MATLAB. The performance of the algorithm has been evaluated by calculating the Signal-to-Noise Ratio (SNR) and the Mean Squared Error (MSE). The effectiveness of the algorithm in reducing the EMG noise in the ECG signals has been demonstrated by the results. The algorithm's limitations and future work were discussed in this paper. Interesting future works could include using other filtering techniques to enhance the performance or deep learning techniques to improve noise cancellation. Overall, the effectiveness of using signal processing techniques to filter out EMG noise from ECG signals has been demonstrated and resulted in clearer and more accurate signals for diagnostic purposes.
Published Version
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