There exist different methods to analyze and study biomedical signals. Some of these methods are based on medical imaging, involving Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Nuclear Scintigraphy, etc. This techniques show the image of a specific part of the human body. But there exits other methods which study the different signals based in 1-dimensional analysis, such as Ele...

This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantane...

Introduction A common problem in biomedical signal processing is signal detection in noisy data. If approximate signal features such as shape and duration are known, the signal’s presence and position in time can be estimated by pattern recognition techniques. A matched filter approach, already shown to aide identification of physiological signals embedded within white [l] or colored 121 noise ...

Correspondence: [email protected] Nalecz Institute of Biocybernetics and Biomedical Engineering PAS, Warsaw, Poland Abstract Background: In analysis of nonstationary nonlinear signals the classical notion of frequency is meaningless. Instead one may use Instantaneous Frequency (IF) that can be interpreted as the frequency of a sine wave which locally fits the signal. IF is meaningful for mon...

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Advances in speech signal analysis facilitate the development of techniques for remote biomedical voice assessment. However, the performance of these techniques is affected by noise and distortion in signals. In this paper, we focus on the vowel /a/ as the most widely-used voice signal for pathological voice assessments and investigate the impact of four major types of distortion that are commo...

This contribution presents a proposal for generating linguistic reports based on the study of biomedical signals of human patients. Although this topic is dealt in many previous works, there are challenges still open for the scientific community, such as the development of systems to produce reports and alerts using a human-friendly language. We present a brief review of some relevant previous ...

the objective of this study is development of driver’s sleepiness using visually evoked potentials (vep). vep computed from eeg signals from the visual cortex. we use the steady state veps (ssveps) that are one of the most important eeg signals used in human computer interface systems. ssvep is a response to visual stimuli presented. we present a classification method to discriminate between cl...

Time-frequency techniques constitutes a major improvement in signal analysis, namely at the field of biomedical signals in which the interdisciplinary nature of the proposed questions implies the development of new strategies to answer to specific problems. Timefrequency analysis using Wavelets, Wigner-Ville transform and more recently the HilbertHuang Transform (HHT) constitutes the core of th...

In this study we propose an automatic method for solving convolutive mixtures separation. The independent components are extracted by frequency domain analysis, where the convolutive model can be solved by instantaneous mixing model approach. The signals are reconstructed back in the observation space resolving the ICA model ambiguities. Simulations are carried out to test the validity of the p...