Wavelet Compression of ECG Signals Using SPIHT Algorithm

Abstract

Abstract — In this paper we present a novel approach for waveletcompression of electrocardiogram (ECG) signals based on the set partitioning in hierarchical trees (SPIHT) coding algorithm. SPIHT algorithm has achieved prominent success in image compression. Here we use a modified version of SPIHT for one dimensional signals. We applied wavelet transform with SPIHT coding algorithmon different records of MIT-BIH database. The results show the high efficiency of this method in ECG compression. Keywords — ECG compression, wavelet, SPIHT. I. I NTRODUCTION LECTROCARDIOGRAM (ECG) signal is a very usefulsource of information for physicians in diagnosing heartabnormalities. With the increasing use of ECG in heart diagnosis, such as 24 hour monitoring or in ambulatorymonitoring systems, the volume of ECG data that should be stored or transmitted, has greatly increased. For example, a 3channel, 24 hour ambulatory ECG, typically has storagerequirement of over 50 MB. Therefore we need to reduce thedata volume to decrease storage cost or make ECG signalsuitable and ready for transmission through commoncommunication channels such as phone line or mobilechannel. So, we need an effective data compression method.The main goal of any compression technique is to achievemaximum data reduction while preserving the significantsignal morphology features upon reconstruction. Datacompression methods have been mainly divided into twomajor categories: 1) direct methods, in which actual signalsamples are analyzed (time domain), 2) transformationalmethods, in which first apply a transform to the signal and dospectral and energy distribution analysis of signals.Examples of direct methods are: differential pulse codemodulation (DPCM), amplitude zone time epoch coding (A TEC), turning point, coordinate reduction time encoding system (CORTES), Fan algorithm, ASEC. Reference [1] is agood review of some direct compression methods used inECG compression.