ECG SIGNAL CODING USING BIORTHOGONAL WAVELET-BASED BURROWS-WHEELER CODER

作者：Kumari R Shantha Selva^*; Prabha R Suriya; Sadasivam V

来源：International Journal of Wavelets, Multiresolution and Information Processing, 2011, 9(2): 269-281.

DOI：10.1142/S0219691311004079

• ECG signal
• biorthogonal wavelets
• Burrows-Wheeler Coder
• compression ratio
• Percent Root mean square Difference

Wavelets are the powerful tool for signal processing especially bio-signal processing. Wavelet transform is used to represent the signal to some other time frequency representation better suited for detecting and removing redundancies. In this paper, electrocardiogram (ECG) signal coding using biorthogonal wavelet-based Burrows-Wheeler Coder is discussed. Biorthogonal wavelet transform is used to decompose the ECG signal. Then the Burrows-Wheeler Coder is applied in order to compress the decomposed ECG signal. The Burrows-Wheeler Coder is the combination of Burrows-Wheeler Transformation (BWT), Move-to-Front (MTF) coder and Huffman coder. Compression Ratio (CR) and Percent Root mean square Difference (PRD) are used as performance measures. ECG signals/records from MIT-BIH arrhythmic database are used to evaluate the performance of this coder. This algorithm is tested with 25 different records from MIT-BIH arrhythmia database and obtained the average PRD as 0.0307% to 3.8706% for the average CR of 3.6362 : 1 to 280.48 : 1. For record 117, the CR of 8.1638 : 1 is achieved with PRD 0.1652%. This experimental results show that this coder outperforms other coders such as Djohn, EZW, SPIHT, Novel algorithm etc. that exist in the literature in terms of coding efficiency and computation.

• 出版日期2011-3