ECG data compression techniques-a unified approach

Abstract

A broad spectrum of techniques for electrocardiogram (ECG) data compression have been proposed during the last three decades. Such techniques have been vital in reducing the digital ECG data volume for storage and transmission. These techniques are essential to a wide variety of applications ranging from diagnostic to ambulatory ECG's. Due to the diverse procedures that have been employed, comparison of ECG compression methods is a major problem. Present evaluation methods preclude any direct comparison among existing ECG compression techniques. The main purpose of this paper is to address this issue and to establish a unified view of ECG compression techniques. ECG data compression schemes are presented in two major groups: direct data compression and transformation methods. The direct data compression techniques are: ECG differential pulse code modulation and entropy coding, AZTEC, Turning-point, CORTES, Fan and SAPA algorithms, peak-picking, and cycle-to-cycle compression methods. The transformation methods briefly presented, include: Fourier, Walsh, and K-L transforms. The theoretical basis behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, differential pulse code modulation (DPCM), and entropy coding methods. The paper concludes with the presentation of a framework for evaluation and comparison of ECG compression schemes.

High Speed ECG Image Compression Using Modified SPIHT

“…Each node has either four offspring or no offspring. The nodes has no offspring are located on Layer 1 (i.e., the bands HL 1 , LH1 and HH 1) and some of them are located on the highest layer (one of them indicated by the " * " in Fig. 1).…”

An Efficient Technique for Compressing ECG Signals Using QRS Detection, Estimation, and 2D DWT Coefficients Thresholding

“…These compression techniques can be broadly classified into three groups: (1) direct data handling techniques [1][2][3], (2) transformationbased techniques [3][4][5][6][7][8][9][10][11], and (3) parameterized modelbased techniques [9]. Our approach belongs to the second group.…”

“…In recent years, many schemes have been suggested for compression of ECG data [1][2][3][4][5][6][7][8][9][10][11]. These compression techniques can be broadly classified into three groups: (1) direct data handling techniques [1][2][3], (2) transformationbased techniques [3][4][5][6][7][8][9][10][11], and (3) parameterized modelbased techniques [9].…”

Hierarchical Neural Network Based Compression of ECG Signals

“…The continuing proliferation of computerized electrocardiogram (ECG) processing systems along with the increased feature performance requirements and demand for lower cost medical care have mandated reliable, accurate, and more efficient ECG data compression techniques. The practical importance of ECG data compression has become evident in many aspects of computerized electrocardiography including: a)increased storage capacity of ECG's as databases for subsequent comparison or evaluation, b) feasibility of transmitting real-time ECG's over the public phone network, c) implementation of cost effective real-time rhythm algorithms, d)economical rapid transmission of off-line ECG's over public phone lines to a remote interpretation center, and e) improved functionality of ambulatory ECG monitors and recorders [1].…”

“…Recently some of the frequency domain techniques offer good compression ratios, generally at the expense of more computations [1,2,3]. G.E.…”