This paper presents a new algorithm for electrocardiogram (ECG) signal compression based on local extreme extraction, adaptive hysteretic filtering and Lempel-Ziv-Welch (LZW) coding. The algorithm has been verified using eight of the most frequent normal and pathological types of cardiac beats and an multi-layer perceptron (MLP) neural network trained with original cardiac patterns and tested with reconstructed ones. Aspects regarding the possibility of using the principal component analysis (PCA) to cardiac pattern classification have been investigated as well. A new compression measure called "quality score," which takes into account both the reconstruction errors and the compression ratio, is proposed.

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The paper presents a number of results regarding the construction of specific overcomplete dictionaries for ECG compressed sensing (CS). The dictionaries were built using normal and patological cardiac patterns extracted from 24 recordings of the MIT-BIH Arrhythmia Database. It has been shown that the compression results obtained using the CS concept based on specific dictionaries are better that those using the wavelet overcomplete dictionaries. Starting from the concept of sparse signal with respect to a given overcomplete dictionary the paper present several results regarding the possibility of simple pattern classification as well

In this paper we investigate the reconstruction of compressed sensed ECG signals using dictionaries specific for each patient. Two projection matrices, random and Bernoulli, have been tested. For 24 records from the MIH-BIH database and for a compression ratio (CR) of 10:1 an average percentage rootmean-square difference (PRD) of 0.81, a normalized PRD (PRDN) of 15.49 and a quality score (QS) of 12.34 have been obtained. For record 117, for CR = 15:1 the PRD, PRDN and QS were 0.96, 17.28 and 15.62 respectively.

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