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- W4375946951 abstract "Heart disease detection mainly depends on electrocardiogram (ECG) signals. Thus, patient information is recorded at higher bit resolutions to detect various heart issues, which causes difficulties in the storage and transmission of data. In this regard, this work proposes an efficient reconstruction algorithm for SVD based compression method, which significantly improves the compression and reconstruction performance for the 2D ECG signals. Although, several compression algorithms are proposed for 2D ECG signals, but lag in improving the reconstruction performance. The proposed algorithm uses singular value thresholding (SVT) to improve the reconstruction performance. It is efficient to reconstruct a low rank 2D array from very few entries. In this way, a low rank approximation of ECG array is achieved by SVD, followed by sparse sampling up to a desired level. The reconstruction is performed by applying the SVT algorithm on the sparse samples selected from the low rank 2D ECG array. The proposed methodology is implemented on the widely used MIT-BIH arrhythmia database. It is tested to reconstruct the signal by observing 30%, 40%, 50%, and 60% samples, and the obtained CR is 17.01, 12.77, 10.22, and 8.52, respectively, which ensure the effectiveness of the proposed experimental work." @default.
- W4375946951 created "2023-05-10" @default.
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- W4375946951 date "2023-03-23" @default.
- W4375946951 modified "2023-09-29" @default.
- W4375946951 title "Singular Value Thresholding based Sparse Reconstruction Algorithm for 2D ECG Signals" @default.
- W4375946951 doi "https://doi.org/10.1109/spin57001.2023.10117168" @default.
- W4375946951 hasPublicationYear "2023" @default.
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