The electrocardiogram (ECG) signal is considered as one of the most important tools in clinical practice in order to assess the cardiac status of patients. In this study, an improved QRS (Q wave, R wave, S wave) complex detection algorithm is proposed based on the multiresolution wavelet analysis. In the first step, high frequency noise and baseline wander can be distinguished from ECG data based on their specific frequency contents. Hence, removing corresponding detail coefficients leads to enhance the performance of the detection algorithm. After this, the author's method is based on the power spectrum of decomposition signals for selecting detail coefficient corresponding to the frequency band of the QRS complex. Hence, the authors have proposed a function g as the combination of the selected detail coefficients using two parameters λ 1 and λ 2 , which correspond to the proportion of the frequency ranges of the selected detail compared with the frequency range of the QRS complex. The proposed algorithm is evaluated using the whole arrhythmia database. It presents considerable capability in cases of low signal-to-noise ratio, high baseline wander and abnormal morphologies. The results of evaluation show the good detection performance; they have obtained a global sensitivity of 99.87%, a positive predectivity of 99.79% and a percentage error of 0.34%.

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This paper presents a method to characterize, identify and classify some pathological Electroencephalogram (EEG) signals. We use some Time Frequency Distributions (TFDs) to analyze its nonstationarity. The analysis is conducted by the spectrogram (SP), the Choi–Williams Distribution (CWD) and the Smoothed Pseudo Wigner Ville Distribution (SPWVD). The studies are carried on some real EEG signals collected from a known database. The estimation of the best value of parameters for each distribution is achieved using the Rényi entropy (RE). The time-frequency results have permitted to characterize some pathological EEG signals. In addition, the Rényi Marginal Entropy (RME) is used for the purpose of detecting the peak seizures and discriminates between normal and pathological EEG signals. The frequency bands are evaluated using the Marginal Frequency (MF). The EEG signal classification of two sets A and E containing normal and pathologic EEG signals, respectively, is performed using our proposed method based on energy extraction of signals from time-frequency plane. Also, the Moving Average (MA) is used as a tool to obtain better classification results. The results conducted on real-life EEG signals illustrate the effectiveness of the proposed method.

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The SPIHT (set partitioning in hierarchical trees) algorithm has attracted great attention in recent years as a technique for image coding. Not only does it give good objective and subjective performance, it is also simple and efficient. In this paper we propose an enhanced image lossless image compression that is based on SPIHT The most important modification in this algorithm is the addition of a simple modification to the set of type A with a new test on the threshold . Our experiments show that this improvement increases the performance of lossless image coding for all standard test images.