Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions....

In all developing countries, the application of biomedical signals has been growing, and there is a potential interest to apply it healthcare management systems. However, with existing infrastructure, system will not provide high-end support for transfer by using communication medium, as need be classified at appropriate stages. Therefore, this article addresses issues physical Hadoop-based sys...

This paper proposes the design of micro power Sigma-delta modulator with using verilog HDL based on been mapped on small commercially available FPGAs (Field Programmable Gate Arrays). This Sigma-delta modulator design is paid special attention to its low power application of portable electronic system in digitizing biomedical signals such asElectrocardiogram(ECG),Electroencephalogram(EEG) etc. ...

The methods of Computational Intelligence (CI) including a framework of Granular Computing, open promising research avenues in the realm of processing, analysis and interpretation of biomedical signals. Similarly, they augment the existing plethora of “classic” techniques of signal processing. CI comes as a highly synergistic environment in which learning abilities, knowledge representation, an...

BACKGROUND Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biome...

Blind Source Separation (BSS) is a statistical approach to separating individual signals from an observed mixture of a group of signals. BSS relies on only very weak assumptions on the signals and the mixing process (hence the “blind” descriptor) and this blindness enables the technique to be used in a wide variety of situations. Research in the field of Blind Source Separation has resulted in ...

This paper investigates the applicability of high order statistical autoregressive (AR-HOS) modeling method in analyzing biomedical signals. The autoregressive (AR) method using linear prediction and AR-HOS method using cumulants are applied on normal and pathological heart sound signals. It is found that the AR-HOS modeling a signal produce more accurate and higher resolution spectrum than AR ...

serious concerns have been expressed about potential health risks of nano silver containing consumer products (agnps) therefore regulatory health risk assessment on such nanoparticles has become mandatory for the safe use of agnpsinbiomedicalproducts with special concerns to the mutagenic potentials. in this study, we examined the inhibitory and mutagenicity effects of agnps in three different ...

This set of exercises is about the reasons why nonlinearity can be important in interpreting biomedical signals. We will use both linear and simple nonlinear techniques to study some model signals and see how rather simple nonlinear mechanisms can lead to behavior that is profoundly different from that of linear systems. Later in other exercises, we will use more sophisticated nonlinear techniq...