Key result
A novel algorithm combining data extraction and truncated singular value decomposition allows for adjustable ECG data compression while keeping reconstruction error low.
A novel algorithm combining data extraction and truncated singular value decomposition offers an adjustable method for ECG data compression with low error.
Enables adjustable ECG compression for efficient storage; leaves open clinical workflow validation before adoption.
In this paper a novel algorithm is used for compressing ECG data. The algorithm uses two techniques simultaneously to get a better compression of ECG data. Firstly Data extraction: a sampling technique based on a user defined threshold value is applied on ECG followed by Truncated Singular Value Decomposition. Changing the number of Singular Values higher compression can be achieved. The proposed method provides two variables: Threshold & Number of Singular Value, which can be changed to achieve optimum compression while keeping the error low. Compression performance & quality of reconstructed ECG in the proposed method is evaluated using Compression Ratio (CR) & Percent Root Mean Square Difference (PRD).
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Kabir et al. (2017) studied ECG data compression. Data extraction and Truncated Singular Value Decomposition algorithm was evaluated on Compression Ratio (CR) and Percent Root Mean Square Difference (PRD). A novel algorithm combining data extraction and truncated singular value decomposition allows for adjustable ECG data compression while keeping reconstruction error low.
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