An ECG signals compression method and its validation using NNs.
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.
Fira
Catalina Monica
CM
Institute of Computer Science, Iasi, Romania. mfira@scs.etc.tuiasi.ro
Goras
Liviu
L
D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted