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In this paper, we present a simple and efficient normalized signed regressor LMS (NSRLMS) algorithm, that can be applied to ECG signal in order to remove various artifacts from them. This algorithm enjoys less computational complexity because of the sign present in the algorithm and good filtering capability because of the normalized term. As a result it is particularly suitable for applications requiring large signal to noise ratios with less computational complexity. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio.

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Disease detection is a critical issue in the field of medical diagnostics. Failure to identify heart disease (HD) at an early stage can lead to mortality. The lack of access to expert physicians in remote areas further exacerbates the problem. Big data analytics (BDA) is an emerging area that can help extract valuable information from vast amounts of data and improve medical diagnosis while reducing costs. Machine learning (ML) algorithms have been effectively employed in many fields, including medical diagnostics. ML methods can help doctors detect and forecast illnesses at an early stage by creating classifier systems. In this article, we propose a unique ML- and BDA-based squirrel search-optimized Gradient Boosted Decision Tree (SS-GBDT) for the detection of heart disease. The effectiveness of the proposed method is demonstrated through various performance indicators. The results show that the proposed method is highly efficient in medical diagnosis, with 95% accuracy rate, 95.8% precision, 96.8% recall and 96.3% F1-measure achieved by the SS-GBDT. The use of BDA and ML can greatly enhance medical diagnosis and this proposed method is a significant step in this direction.