Nowadays, ECG monitors represent necessary diagnostic devices. A typical electrocardiogram monitoring device generates massive volumes of digital data. Thus, the compression of ECG signal data is of the great practical significance. This paper presents an ECG compression method for efficient ECG signal transmission via GPRS network. It is based on predictive coding using ARX model. Tests on clinically measured ECG signals confirm a very good performance in terms of compression ratio

An Efficient Approach to ECG Signal Transmission via GPRS

In the modern hospital, the efficient storage of electronically recorded biomedical signals as well as its transmission over communication networks is becoming more and more important. Although digital storage media is currently almost inexpensive and computational power has exponentially increased in last years, effective electrocardiogram (ECG) compression techniques are still very attractive. In fact, several millions of electrocardiograms are recorded annually and the transfer of electrocardiogram records over communication networks for remote analysis is now done more than ever. Besides the increased storage capacity for archival purposes, ECG compression allows real-time transmission over communication networks, economic off-line transmission to remote interpretation sites and enables efficient HCG rhythm analysis algorithms. In this paper, an adaptive approach to ECG compression is proposed, which provides comparable results with various types of ECG signal, namely normal sinus rhythm, ventricular tachycardia and ventricular fibrillation. Most proposals in the literature approach the problem of ECG compression without considering the possibility of such pathologies and, consequently, their performance deteriorate in cases where such signals are present. Experimental results of the proposed algorithm are promising, although compression ratios are still not yet as good as they can get.

A predictive adaptive approach to generic ECG data compression

This paper proposes a methodology for ECG (electrocardiograms) data compression based on R-R segmentation. An ECG can be seen as a quasi-periodic signal, where it is possible to find many similarities between heart beats. These similarities are explored by the proposed compression scheme through the use of a segment dictionary combined with an efficient form of progressive error codification. The dictionary is able to incorporate new patterns, in order to assure the algorithm adapts to changes in signal morphology. Experimental results reveal that high compression ratios are possible for highly regular signals, with irregular signals still achieving acceptable results.

An ECG compression approach based on a segment dictionary and bezier approximations

Computer-assisted arrhythmia detection is crucial for the treatment of cardiac disorders. Electrocardiograms (ECG) are used to study the electric heart activity and diagnose abnormalities in the heart. It is a non-invasive method where the electric signal of the heart is captured through electrodes placed on the skin. In this study the ECG signals will be classified according to the heart abnormalities using the Generalized Autoregressive Moving Average (GARMA) and Generalized Linear Model (GLM). The ECG features were extracted using the GARMA model. The coefficients obtained from GARMA model will be classified using the GLM model to detect and classify into normal and five types of arrhythmias.

GARMA Modeling of ECG and Classification of Arrhythmia

This work describes a lossy electrocardiogram (ECG) compression algorithm based on R-R segmentation and segment matching. An ECG can be thought of as a quasi-periodic signal, with many similarities existing between heartbeats acquired from the same source. Through the use of an adaptive dictionary, it is possible to explore the similarities between new and previously encountered patterns, incorporating new patterns when a significant change in morphology has been observed. Algorithm simulation reveals very high compression ratios are possible on very regular signals.

ECG Compression through segment matching and progressive error encoding

Electrocardiogram (ECG) signal compression suffers of lack of standards for analogue-digital conversion. Results of this study have shown that 8 bits/sample, although frequently in use, does not satisfy quality criteria for medical doctors. This paper also presents predictive technique for lossless ECG compression using linear time-invariant models. Tests on clinically measured ECG signals confirm a very good performance in terms of compression ratio.

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Miroslav Despotovic

Papers

Predictive ECG coding using linear time-invariant models