A novel linear discriminant analysis-based classification of R-peaks in different ECG signal datasets

In the current scenario, there is a need to develop efficient pre-processing and classification techniques which can form the basis of an automated health monitoring system. In this paper, independent component analysis (ICA) is proposed to be used for electrocardiogram (ECG) signal processing as reported by the same authors, who found it to yield better results that time for limited datasets. Here, it has been applied on a variety of datasets, viz., real and standard and the obtained results are compared with those obtained using another widely used and reported technique, viz., adaptive notch filter (ANF) in the literature. For classification, linear discriminant analysis (LDA) is proposed to be used as it performs multi-class classification tasks better. The obtained results demonstrate the utility of the proposed methodology for bioinformatics community, especially during critical heart surgeries and designing of evolving healthcare systems in future.