One of the biggest challenges in averaging ECG or EEG signals is to overcome temporal misalignments and distortions, due to uncertain timing or complex non-stationary dynamics. Standard methods average individual leads over an collection of epochs on a time-sample by timesample basis, even when multi-electrode signals are available. Here we propose a method that averages multi electrode recordi...

The problem of recognition and classification biomedical signals is a complex related to the interdisciplinary field computer science medicine. Within framework project implementation development new defibrillation equipment, it necessary solve problems analyzing electrocardiogram obtain diagnostic solution with possibility assigning specific condition pathological patient. This article present...

Biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with Hilbert-Huang Transform provides a fully adaptive and data-driven technique to extract Intrinsic Mode Functions (IMFs). The latter represent a complete set of locally orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time serie...

Digital Signal Processing techniques constitute the basic scientific approach used in most of the current advances in medicine. In particular, the development of algorithms in order to extract, predict and model raw biomedical data series has revolutionized many routine, but data-intensive, areas of current medical practice. In this contribution, we present an evolutionary technique for modelli...

A fractal signal x(t) in biomedical engineering may be characterized by 1/f noise, that is, the power spectrum density (PSD) divergences at f = 0. According the Taqqu's law, 1/f noise has the properties of long-range dependence and heavy-tailed probability density function (PDF). The contribution of this paper is to exhibit that the prediction error of a biomedical signal of 1/f noise type is l...

In this dissertation some advanced methods for extracting sources from single and mul­ tichannel data are developed and utilized in biomedical applications. It is assumed that the sources of interest have periodic structure and therefore, the periodicity is exploited in various forms. The proposed methods can even be used for the cases where the signals have hidden periodicities, i.e., the peri...

The paper addresses two problems that are frequently encountered when modeling data by linear combinations of nonlinear parameterized functions. The first problem is feature selection, when features are sought as functions that are nonlinear in their parameters (e.g. Gaussians with adjustable centers and widths, wavelets with adjustable translations and dilations, etc.). The second problem is t...

Advances in biomedical research over recent decades have substantially raised expectations that the pharmaceutical industry will generate increasing numbers of safe and effective therapies. However, there are warning signs of serious limitations in the industry's ability to effectively translate biomedical research into marketed new therapies. Clinical pharmacologists should be aware of these s...

Fateme Pourhasanzade, Seyed Hojjat Sabzpoushan, Ali Mohammad Alizadeh, Ebrahim Esmati a Bioelectric Department, Research laboratory of Biomedical signals and sensors, Iran University of Sciences and Technology (I.U.S.T), Tehran, Iran b Bioelectric Department, Biomedical Engineering Faculty, Iran University of Sciences and Technology (I.U.S.T), Tehran, Iran c Cancer Research Center, Tehran Unive...

Somatosensory evoked potentials (SEPs) were obtained from 15 rats that were inflected with graded injury using the NYU Impactor. SEPs, in response to stimuli to the nerves in the limbs, were recorded from the cranium. Spectral coherence was used to analyze these signals. Results obtained from the investigation show that this technique is capable of providing a quantitative measure of spinal cor...