Key result
The recursive least squares (RLS) algorithm with an optimized lambda between 0.05 and 0.9 was more effective than other methods in ECG noise cancellation, providing faster convergence.
The optimized RLS algorithm provides superior ECG noise reduction and waveform conservation compared to other adaptive filtering methods.
May aid ECG interpretation via superior noise cancellation; leaves open prospective validation before clinical adoption.
The electrocardiogram (ECG) is generally used for the diagnosis of cardiovascular diseases. In many of the biomedical applications, it is necessary to remove the noise from ECG recordings. Several adaptive filter structures have been proposed for noise cancellation. Compared to the least mean square (LMS) method, the unbiased and normalized adaptive noise reduction (UNANR) algorithm has better performance, as mentioned in previous investigations. In this paper, we review various kinds of ECG noise reduction algorithms. To provide a detailed and fair comparison, all normalized LMS (NLMS), Block LMS (BLMS), recursive least squares (RLS) and UNANR algorithms are implemented and their performance have been assessed using the same dataset and compared to different state-of-the-art approaches. Then, the performance analysis of all five algorithms is presented and compared in term of mean squared error (MSE), computational complexity and stability. The obtained results revealed that RLS method is much more effective and powerful than other methods in ECG noise cancellation, and even better than UNANR. Then, in order to reach the best performance of the mentioned filter and also, to minimize the output signal error, the optimized parameters of the algorithm were defined and results were investigated. The obtained outcomes show that the best Lambda (λ) occurs between 0.05 and 0.9, so that the convergence rate of the optimized RLS filter is faster than others. It not only decreases the noise, but also the ECG waveform is better conserved. Furthermore, the introduced optimized method with adaptive threshold value would have great potential in biomedical application of signal processing and other fields.
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Ebrahimzadeh et al. (2015) studied Cardiovascular diseases (ECG noise removal). Recursive least squares (RLS) algorithm vs. NLMS, BLMS, and UNANR algorithms was evaluated on Mean squared error (MSE), computational complexity, and stability. The recursive least squares (RLS) algorithm with an optimized lambda between 0.05 and 0.9 was more effective than other methods in ECG noise cancellation, providing faster convergence.
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