Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation

This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC) detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances

Emission Modelling for Supervised ECG Segmentation using Finite Differences

Abstract — The segmentation of ECG signals into P waves, QRS complexes, T waves and baselines is an important practical problem for physicians diagnosing cardiac diseases. The duration of the signal and the number of beats to segment are often too large for a manual annotation, so that automatic segmentation is a challenging and useful tool. State-of-the-art algorithms use hidden Markov models with wavelet transform encoding and represent the ECG in multidimensional spaces using Gaussian mixtures models. The main problem of this approach is its computational cost due to the number of free parameters, the choice of the wavelet transform parameters and the high failure rate of the EM algorithm. In this work, we propose an alternative emission encoding for hidden Markov models using both the ECG signal and its derivative in order to better model the dynamics of the signal in a lower dimensional space. We show that this method achieves similar performances with much less model parameters and is less subject to failures

Utilização e recuperação de sílica gel impregnada com nitrato de prata Use and recycling of silica gel impregnated with silver nitrate

<abstract language="eng">Argentation chromatography is used to increase the selectivity of the chromatographic process, chiefly in the resolution of complex mixtures of nonpolar substances. Although efficient, this technique generates residues containing heavy metal which makes its discarding through common procedures impracticable. In the present work a simple method for recycling of silica, and also silver, from argentation chromatography is described. This procedure uses initially a treatment of H2O2/HNO3, with subsequent treatment with H2O2/H2SO4 , allowing an efficient recycling of both components. This methodology is simple, costless, removes impurities efficiently, and does not modify retention parameters nor specific surface in a significant way