The most prominent type of artifact contaminating electroencephalogram (EEG) signals is the eye blink (EB) artifact. Hence, EB artifact detection is one of the most crucial pre-processing step in EEG signal processing before this artifact can be removed. In this work, an approach that identifies EB artifacts without human supervision and automated varying threshold setting is proposed and evaluated. The algorithm functions on the basis of correlation between two EEG electrodes, Fp1 and Fp2, followed by EB artifact threshold determination utilizing the amplitude displacement from the mean. The proposed approach is validated and evaluated in terms of accuracy and error rate in detecting events of EB artifacts in EEG signals. Analysis has revealed that the proposed approach achieved an average of 96.6% accuracy compared to a conventional method of identifying EB artifacts with a fixed constant threshold.

This paper addresses the noise estimation in the digital domain and proposes a noise estimator based on the step signal model. It is efficient for any distribution of noise because it does not rely only on the smallest amplitudes in the signal or image. The proposed approach uses polarized/directional derivatives and a nonlinear combination of these derivatives to estimate the noise distribution (e.g., Gaussian, Poisson, speckle, etc.). The moments of this measured distribution can be computed and are also calculated theoretically on the basis of noise distribution models. The 1D performances are detailed, and as this paper is mostly dedicated to image processing, a 2D extension is proposed. The 2D performances for several noise distributions and noise models are presented and are compared with selected other methods.

Electrocardiogram (ECG) signals are used to analyze the cardiovascular activity in the human body and have a primary role in the diagnosis of several heart diseases. The QRS complex is the most important and distinguishable component in the ECG because of its spiked nature and high amplitude. Automatic detection and delineation of the QRS complex in ECG is of extreme importance for computer aided diagnosis of cardiac disorder. Therefore, the accurate detection of this component is crucial to the performance of subsequent machine learning algorithms for cardiac disease classification. The aim of the present work is to detect the QRS wave from electrocardiogram (ECG) signals. Initially the baseline drift has been removed from the signal followed by the decomposition using continuous wavelet transform. Modulus maxima approach proposed by Mallat has been used to compute the Lipschitz exponent of the components. By using the property of R peak, having highest and prominent amplitude and Lipschitz exponents, we have applied the K means clustering technique to classify QRS complex. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database.

Electrocardiogram (ECG) signal is used to analyze the cardiovascular activity in the human body and has a primary role in the diagnosis of several heart diseases. The QRS complex is the most distinguishable component in the ECG. Therefore, the accuracy of the detection of QRS complex is crucial to the performance of subsequent machine learning algorithms for cardiac disease classification. The aim of the present work is to detect QRS wave from ECG signals. Wavelet transform filtering is applied to the signal in order to remove baseline drift, followed by QRS localization. By using the property of R peak, having highest and prominent amplitude, we have applied thresholding technique based on the median absolute deviation(MAD) of modulus maximas to detect the complex. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database. The results have been examined and approved by medical doctors.