An unsupervised technique using PCA, Savitzky Golay filter, beta wavelet transform, and run-length encoding was proposed for cardiac signal compression to support e-healthcare systems.
The proposed unsupervised technique using PCA and beta wavelet transform improves cardiac signal compression for telemedicine applications.
For a healthy society health of a human being needs to be taken care of. The smart age needs to provide adequate support to people in every respect. In this paper, authors have taken an approach to support e-healthcare systems in terms of cardiac signal compression that helps with data storage and communication. The compression method is performed using principal component analysis (PCA) that reduces the data redundancy. Initially, the Savitzky Golay filter (S Golay) is used for the pre-processing of the raw signals that follow the PCA application, further, the beta wavelet transform is put to use for thresholding purposes and to obtain exact information of the cardiac signal from the principal components. Finally, run-length encoding (RLE) is used that helps to communicate the data bits. The proposed method was found better, that comparison is demonstrated in the result section.
Baliarsingh et al. (Tue,) conducted a other in Cardiac signal compression. Unsupervised technique for cardiac data compression with β wavelet transform was evaluated on Data compression performance. An unsupervised technique using PCA, Savitzky Golay filter, beta wavelet transform, and run-length encoding was proposed for cardiac signal compression to support e-healthcare systems.