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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. Index Terms--Biomedical signal processing, data compression, neural networks (NNs), signal processing.

The Problem of linear inductance- and capacitance-time (L/T, C/T) conversions is approached through the systematic study of four possibilities to build astable multivibrators using piecewise linear resistances obtained from the one operational amplifier (OA) negative impedance converter (NIC) configuration. A new L/T converter with grounded inductance is found. Formulas for the time period taking into consideration the losses as well as the OA saturation output resistance are derived.