An Efficient Threshold Prediction Scheme for Wavelet Based ECG Signal Noise Reduction Using Variable Step Size Firefly Algorithm

Electrocardiographic (ECG) signal is significant to diagnose cardiac arrhythmia among various biological signals. The accurate analysis of noisy Electrocardiographic (ECG) signal is very motivating challenge. Prior to automated analysis, the noises present in ECG signal need to be eliminated for accurate diagnosis. Many researchers have been reported different methods for denoising the ECG signal in recent years. In this paper, an optimized threshold mechanism is proposed for wavelet based medical signal noise reduction. This scheme is based on a variable step size firefly algorithm (VSSFA) in dual tree complex wavelet scheme, in which the VSSFA is utilized for threshold optimization. This approach is evaluated on several normal and abnormal ECG signals of MIT/BIH arrhythmia database, by artificially adding white Gaussian noises with variation of 5dB and 10dB. Simulation result illustrate that the proposed system is well performance in various noise level, and obtains better visual quality compare with other methods.