An ECG Signals Compression Method and Its Validation Using NNs

Abstract

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.

On the Projection Matrices Influence in the Classification of Compressed Sensed ECG Signals

“…In other words, ECG segments will be used (cardiac cycles) and the ECG signal will be reconstructed by concatenating these cardiac segments (cycles). According to the algorithm described in [9] the segmentation of the ECG signal into cardiac cycles is achieved based on the R waves detection. Thus, one cardiac cycle is represented by the ECG signal between the middle of a RR segment and the middle of the next RR segment, where the RR segment means the ECG waveform between two successive R waves.…”

“…After the segmentation of the ECG signal there is a centering of the R wave which is made by resampling on 150 samples on both sides of the R wave. In this way all cardiac cycles will have size 301 and the R wave will be positioned on the sample 151 [9]. Figure 1.…”

A Review of ECG Data Compression Techniques

“…Thus original samples of ECG are subjected to a transformation and the compression is performed in the entirely new domain like Fourier transform (FT), DCT and wavelet etc [8,12,16,28,31]. These techniques pose higher CR than direct techniques and are insensitive to noise present in ECG signals.…”

ECG signal compression using wavelets. Preliminary results

“…We defined a quality factor QF similar with the measure proposed in [9] as the ratio of the square of the compression ratio and the distortion factor and we computed it for different groups of three features: 2 / CR PRD QF =…”