This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transform and the adaptive threshold technique are applied for QRS detection. Finally, the Principal Component Analysis is implemented to extract features from the ECG signal. Nineteen different records from the MIT-BIH arrhythmia database have been used to test the proposed method. A 96.28% of sensitivity and a 99.71% of positive predictivity are reported in this testing for QRS complexity detection, being a positive result in comparison with recent researches. All Rights Reserved

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Column buckling mechanics were examined as a technique to determine the modulus of glassy polymer films that fail at very low strains in tension. As an alternative modulus measurement technique, free-standing column buckling (FSCB) mechanics were investigated here. Given the film geometries and the critical buckling load, classical relationships can be used to determine the modulus. Several polymeric materials were tested and compared to uniaxial tensile values to determine the robustness and validity of the technique. Film geometries were varied from 4 to 18 mm in width and from 15 to 60 mm in length. The films were compressed in plane until buckling occurred and the critical buckling load was measured for each geometry. The critical buckling load increased as film width increased and decreased as film length increased, while the thickness was held constant for each material. For polyethylene terephthalate films, the elastic modulus was determined to be 3.06 AE 0.58 GPa. This FSCB-determined modulus was compared to the elastic modulus obtained by tensile testing (3.54 AE 0.2 GPa). The modulus measurement technique presented here has the potential to be used experimentally to determine the elastic modulus of glassy polymer films that perform poorly in tension.

This paper presents a study of the dynamic (recurrent) quadratic neural unit (QNU) -a class of higher order network or a class of polynomial neural network-as applied to the prediction of lung respiration dynamics. Human lung motion during respiration features nonlinear dynamics and displays quasiperiodical or even chaotic behavior. An attractive approximation capability of the recurrent QNU are demonstrated on a long term prediction of time series generated by chaotic MacKey-Glass equation, by another highly nonlinear periodic time series, and on real lung motion measured during patients respiration. The real time recurrent learning (RTRL) rule is derived for dynamic QNU in a matrix form that is also efficient for implementation. It is shown that the standalone QNU gives promising results on a longer prediction times of the lung position compared to results in recent literature. In the end, we show even more precise results of two QNUs implemented as two local nonlinear predictive models and thus we present and discus a promising direction for high precision prediction of lung motion.

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