An Effective and Efficient Compression Algorithm for ECG Signals With Irregular Periods

Abstract

This paper presents an effective and efficient preprocessing algorithm for two-dimensional (2-D) electrocardiogram (ECG) compression to better compress irregular ECG signals by exploiting their inter- and intra-beat correlations. To better reveal the correlation structure, we first convert the ECG signal into a proper 2-D representation, or image. This involves a few steps including QRS detection and alignment, period sorting, and length equalization. The resulting 2-D ECG representation is then ready to be compressed by an appropriate image compression algorithm. We choose the state-of-the-art JPEG2000 for its high efficiency and flexibility. In this way, the proposed algorithm is shown to outperform some existing arts in the literature by simultaneously achieving high compression ratio (CR), low percent root mean squared difference (PRD), low maximum error (MaxErr), and low standard derivation of errors (StdErr). In particular, because the proposed period sorting method rearranges the detected heartbeats into a smoother image that is easier to compress, this algorithm is insensitive to irregular ECG periods. Thus either the irregular ECG signals or the QRS false-detection cases can be better compressed. This is a significant improvement over existing 2-D ECG compression methods. Moreover, this algorithm is not tied exclusively to JPEG2000. It can also be combined with other 2-D preprocessing methods or appropriate codecs to enhance the compression performance in irregular ECG cases.

A Review of ECG Data Compression Techniques

“…Many researchers have proposed ECG compression techniques by treating 1D ECG signal as a 2D image and exploiting the inter-and intra-beat correlations by encoder [37,43,50,51]. These techniques broadly consists steps: QRS detection, ECG segmentation, preprocessing, and transformation.…”

“…In [50], a lossy compression technique based on converting the 1D ECG signal into 2D ECG image have been proposed. A period sorting preprocessing technique was introduced, which consists of a length-based ordering of all periods.…”

A contextual based double watermarking of PET images by patient ID and ECG signal

“…However, the ECG signal has a great amount of redundant data as some of its main features such as the QRS complex are used for many diagnoses procedures. There are many algorithms that compress ECG signal in the range of 20:1 ratio [27,28]. Hence, with the proposed algorithm, it is possible to embed real ECG signal records of a patient (1-4 min) in his/her medical images.…”

Compressão de sinais ECG utilizando DWT com quantização não-linear e por sub-bandas

“…Na atualidade, são utilizados quase exclusivamente mé-todos transformados, que têm os melhores resultados. Existem métodos nos quais se utilizam diferentes transformadas, como a DCT, a DFT [14], métodos de duas dimensões [15] [16] [17], e mais recentemente, a transformada wavelet (WT) [18] [19]. Tendo em conta que existem trabalhos aplicando a WT [18,19,20,21,22,23,24,25,26,27], não se achou na literatura um no qual a estratégia de compressão dos coeficientes transformados fosse dependendo das sub-bandas de decomposição além de levar em consideração a magnitude dos coeficientes (independentemente da sub-banda na qual se encontre).…”

“…Em Chou [17], se realiza um pre-processamento do sinal ECG 1D. Se detectam os complexos QRS e se segmenta o sinal ECG, tomando 130 amostras prévias ao complexo QRS e redistribuindo cada parte segmentada em filas que são parte de uma matriz 2D.…”