ECG signal compression using adaptive prediction

Abstract

A new ECG compression method is presented. First, a prefiltering is effected, followed by a QRS detection. After that the R peaks are localized and the signal is divided into R-R intervals, the original signal can be filtered with less characteristics distortion. This filter is based upon a pondered adaptive long term prediction method. The suggested real-time compression was performed for one of the channels of the MIT/BIH database samples, and can reduce the size of the signal at about 50 bits for one second, without exceeding 10 percent at root mean square reconstruction error (RMSRE). If it is necessary, the algorithm can be used for exact coding, but the size of the concentrated signal highly depends on the sampling rate and resolution. The used adaptive entropy coder introduces about 10 times less redundancy than an optimized Huffman coder.