An opposition-based self-adaptive learning particle swarm optimization algorithm for wavelet-based ECG denoising demonstrated better visual quality and performance compared with other methods.
Does an OSLPSO-based wavelet threshold mechanism improve denoising of ECG signals compared to other methods?
An OSLPSO-based wavelet threshold mechanism effectively reduces noise in ECG signals, potentially improving automated arrhythmia diagnosis.
Electrocardiogram (ECG) signal is significant to diagnose cardiac arrhythmia among various biological signals. The accurate analysis of noisy electrocardiographic (ECG) signal is a very motivating challenge. According to this automated analysis, the noises present in electrocardiogram signal need to be removed for perfect diagnosis. Numerous investigators have been reported different techniques for denoising the electrocardiographic signal in recent years. In this paper, an efficient scheme for denoising electrocardiogram (ECG) signals is proposed based on a wavelet-based threshold mechanism. This scheme is based on an opposition-based self-adaptive learning particle swarm optimisation (OSLPSO) in dual tree complex wavelet packet scheme, in which the OSLPSO is utilised to for threshold optimisation. Different abnormal and normal electrocardiographic signals are tested to evaluate this approach from MIT/BIH arrhythmia database, by artificially adding white Gaussian noises with variation of 5 dB, 10 dB and 15 dB. Simulation results illustrate that the proposed system has good performance in various noise level and obtains better visual quality compared with other methods.
Vinu Sundararaj (2017) studied Cardiac arrhythmia (ECG signal noise reduction). Opposition-based self-adaptive learning particle swarm optimisation (OSLPSO) in dual tree complex wavelet packet scheme vs. Other methods was evaluated on Denoising performance and visual quality. An opposition-based self-adaptive learning particle swarm optimization algorithm for wavelet-based ECG denoising demonstrated better visual quality and performance compared with other methods.