Key result
Implementing a hybrid algorithm on adaptive noise cancellation provided better performance than standard adaptive techniques for enhancing ECG signals.
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A hybrid adaptive filtering algorithm improves the de-noising of ECG signals compared to standard adaptive techniques, which may enhance the accuracy of ECG interpretation.
Electrocardiography (ECG), which is the measure of the electrical activity of the heart, the shape of this signal tells much about the condition of the heart of the patient. Naturally, the ECG signal gets distorted by different artifacts which must be removed otherwise it will convey an incorrect information regarding the patient's heart condition. Several simple and efficient LMS and Normalized LMS adaptive filters that are computationally superior having multiplier free weight update loops are used for cancellation of noise in ECG signals. Implementing Hybrid algorithm on ANC provides better performance than adaptive techniques used to enhance the ECG signal. In this work, fidelity parameters like Signal to Noise Ratio (SNR), MSE, and LSE have to be computed.
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LAXMI et al. (2017) studied ECG signal distortion. Hybrid algorithm on Adaptive Noise Cancellation (ANC) vs. LMS and Normalized LMS adaptive filters was evaluated on Signal to Noise Ratio (SNR), Mean Square Error (MSE), and Least Square Error (LSE). Implementing a hybrid algorithm on adaptive noise cancellation provided better performance than standard adaptive techniques for enhancing ECG signals.
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