2014 Twentieth National Conference on Communications (NCC) 2014
DOI: 10.1109/ncc.2014.6811355
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A new multilead ECG data compression method using Higher-Order Singular Value Decomposition

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Cited by 6 publications
(3 citation statements)
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“…After identifying the dimension where it has most variation, it is possible to determine the best approximation of the original data with fewer dimensions. The SVD [11,12] of a matrix X m×n is given as X = UΣ V T where U ∈ m×m , V ∈ n×n , and Σ m×n = [diag{σ 1 , · · · , σ r }:0], r = min(m, n), and σ 1 , σ 2 , · · · , σ r are the singular values. The reduced SVD of the data matrix can be given as X =Û m×nΣ n×n V T n×n where n and m represent the number of leads and corresponding samples of each lead, respectively [11].…”
Section: Svd-based Training and Testingmentioning
confidence: 99%
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“…After identifying the dimension where it has most variation, it is possible to determine the best approximation of the original data with fewer dimensions. The SVD [11,12] of a matrix X m×n is given as X = UΣ V T where U ∈ m×m , V ∈ n×n , and Σ m×n = [diag{σ 1 , · · · , σ r }:0], r = min(m, n), and σ 1 , σ 2 , · · · , σ r are the singular values. The reduced SVD of the data matrix can be given as X =Û m×nΣ n×n V T n×n where n and m represent the number of leads and corresponding samples of each lead, respectively [11].…”
Section: Svd-based Training and Testingmentioning
confidence: 99%
“…The SVD [11,12] of a matrix X m×n is given as X = UΣ V T where U ∈ m×m , V ∈ n×n , and Σ m×n = [diag{σ 1 , · · · , σ r }:0], r = min(m, n), and σ 1 , σ 2 , · · · , σ r are the singular values. The reduced SVD of the data matrix can be given as X =Û m×nΣ n×n V T n×n where n and m represent the number of leads and corresponding samples of each lead, respectively [11]. TheÛ and V represent the orthonormal basis for the column and row space, respectively.…”
Section: Svd-based Training and Testingmentioning
confidence: 99%
“…The advantages of this method are: it saves a large number of computations of floating point operations and it eases the storage scarcity problem. A preliminary version of this Letter has been reported in [10], where multilevel 3D discrete wavelet transform (DWT) (3D multiscale analysis) has been applied on the MECG tensor. It was difficult to analyse the morphological waves (P-wave, QRS-complex, ST-segment and T-wave) of the ECG signal as the 3D multiscale analysis is unable to segment these waves.…”
mentioning
confidence: 99%