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The non invasive technique, Electrocardiography (ECG), is the principal and prominent tool used for diagnosis and prognosis of heart diseases by physicians and doctors. ECG signal has wide area of applications in biomedical field in determining the functioning of human heart. Thus, ECG signal should be clean and clear to support accurate decisions by cardiologists. As ECG is an electrical signal, so it is susceptible to various kind of noises like low frequency noises (baseline wander) and high frequency noises (power line interference) during its acquisition. From the last few decades, denoising of ECG waveform has been a challenging task in bio-medical research. In this paper, the concept of fractional order calculus (FOC) is employed for denoising of ECG signal. FOC has great potential in biomedical engineering applications, such as image processing and modeling of complex biological systems and processes. FOC applications are concerned with designing digital fractional order differentiators or filters for denoising, important information enhancement and fractal signal generations. In signal processing, the fractional order differentiator (FOD) proves to be an important mathematical tool, which can give more peculiar characteristics as compared to the integer order differentiator. In this paper, Riesz fractional order digital differentiator (RFODD) based on fractional differencing method and Tustin method is used for processing and denoising of ECG signal. RFODD coefficients depend on fractional orders and reduce various artifacts and noises present in it. The pre-processing involves band pass filtering and differentiation of fractional order, which enhances signal to noise ratio (SNR) of ECG waveform. The low pass differentiation removes dc component and high frequency noises. The experimental results demonstrate better quantitative and qualitative results than the state of art methods. The method is tested on several normal and abnormal ECG signals of MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia databases. The performance is validated by adding white Gaussian noise of various dB levels to visually examine the clean and clear ECG waveform. The experimental results show that the proposed method has better results in terms of improved SNR, lower root mean square error (RMSE) and percent root mean square difference (PRD) as compared to the state of art methods in the literature.
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