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As Electrocardiogram (ECG) is a non-stationary signal, therefore its Fourier transform (FT) does not give good time or frequency resolution simultaneously. Also it is well-established by the research communities that the FT is not the most viable tool for non-stationary signals. Hence joint time-frequency analysis tools are introduced to get more accurate results in non-stationary signal environment. ECG is an important tool for providing information about functional status of the heart. The various artifacts and noises, which generally get embedded in ECG during its acquisition and transmission, should be removed at the prior stage. Power line interference, muscle contraction noise, poor electrode contact, patient movement, and baseline wandering are some of the real time noise sources that degrade ECG signal. As the performance of ECG analysing system primarily depends on the accurate and reliable detection of the QRS complex, in addition to T-wave and P-wave, these noises make it difficult for physicians or medical practitioners to evaluate cardiovascular diseases correctly. So in this context, this paper presents a novel approach for ECG signal denoising and QRS localization based on Fractional Stockwell transform (FrST), which is a modified variant of S-transform (ST) in fractional frequency domain. Parameters of FrST help to perform operations in domain where the signal is highly concentrated, so that noise can be easily separated out from ECG signal. The proposed transform has given better time and frequency resolution than the prevailing ST. The concept of Shannon energy is applied after masking and thresholding, instead of classical squared energy to get significant amplitude changes of QRS and to emphasize medium and low QRS beats. To validate the performance of proposed methods, the proposed denoising method is applied on 5dB, 10dB and 15dB input SNR level by corrupting ECG data taken from standard database of MIT-BIH Arrhythmia with additive white Gaussian noise. The simulation results show better Root Mean Square Error, Percent Root Mean Square Difference, and improved Signal to Noise Ratio values, that proves the superiority of proposed denoising method than the existing denoising methods. Also, QRS localization results give much better Sensitivity, Positive Predictivity and Error Rate as compared to the well-established QRS detection methods.
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