Abstract :
Many transform-based compression techniques, such as Fourier, Walsh, Karhunen-Loeve (KL), wavelet, and discrete cosine transform (DCT), have been investigated and devised for electrocardiogram (ECG) signal compression. However, the recently introduced Burrows-Wheeler Transformation has not been completely investigated. In this paper, we investigate the lossless compression of ECG signals. We show that when compressing ECG signals, utilization of linear prediction, Burrows-Wheeler Transformation, and inversion ranks yield better compression gain in terms of weighted average bit per sample than recently proposed ECG-specific coders. Not only does our proposed technique yield better compression than ECG-specific compressors, it also has a major advantage: with a small modification, the proposed technique may be used as a universal coder
Keywords :
electrocardiography; linear predictive coding; medical signal processing; Burrows-Wheeler transformation; ECG signal compression; electrocardiogram; inversion ranks; linear prediction; Cardiac disease; Communication channels; Compressors; Databases; Discrete cosine transforms; Discrete wavelet transforms; Electrocardiography; Monitoring; Sampling methods; Signal processing; BW Transformation; inversions; lossless ECG compression; prediction; Algorithms; Computer Simulation; Data Compression; Diagnosis, Computer-Assisted; Electrocardiography; Humans; Linear Models; Models, Cardiovascular; Signal Processing, Computer-Assisted;
