The aim of electrocardiogram (ECG) compression is to achieve as much compression as possible while the significant information is preserved in the reconstructed signal. Lossy thresholding-based compressors have shown good performance needing low computational resources. In this work, two compression schemes that include nearly perfect reconstruction cosine modulated filter banks for the signal decomposition are proposed. They are evaluated for highly reliable applications, where the reconstructed signal must be very similar to the original. The whole MIT-BIH Arrhythmia Database and suitable metrics are used in the assessment, to obtain representative results. Results show that the proposed compressors yield better performance than discrete wavelet transform-based techniques, when high quality requirements are imposed.
Data Compression/methods , Electrocardiography/methods , Signal Processing, Computer-Assisted , Algorithms , Arrhythmias, Cardiac/physiopathology , Computers , Humans , Models, Statistical , Reproducibility of Results , Software , Wavelet Analysis
