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- W3010698503 abstract "Most of the cardiac arrhythmias are interpreted by observing the abnormal changes in the QRS complex, P and T waves of the noise free ECG signal. But, the recorded ECG signal is contaminated with the other noise sources present in the surrounding environment. These noises within the ECG signal hide the useful information and may mislead to improper diagnosis. This gives the primary importance to de-noise and obtain an interference free ECG signal for accurate diagnosis and proper treatment of arrhythmias. Wavelet shrinkage methods which are widely used de-noising approaches, employ conventional universal threshold value. The main limitation of shrinkage methods is the high universal threshold value due to which noise as well as signal coefficients are suppressed and thus unable to provide optimal solution for de-noising. To overcome this, wavelet transform based Thresholding Neural Network (TNN) is used for adaptive noise reduction which provides a better de-noising procedure by finding the optimal threshold value. In this work, two methodologies are proposed. In the first method Gauss Newton algorithm is implemented in TNN and in the second Newton Raphson algorithm is implemented to obtain the optimal threshold value. The performance of the proposed algorithms are studied using SNR and MSE parameters and the investigated results illustrate that Newton Raphson method is computationally efficient compared to Gauss Newton method." @default.
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- W3010698503 date "2019-03-01" @default.
- W3010698503 modified "2023-09-27" @default.
- W3010698503 title "TNN based Adaptive Learning Algorithms for Precise ECG Signal Processing" @default.
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- W3010698503 doi "https://doi.org/10.1109/wispnet45539.2019.9032853" @default.
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