Biomedical subjects

S N Sarbadhikari

Publications and source records attributed to S N Sarbadhikari.

12 recordsLinked to original sources

PubMed2001

Chaos in the brain: a short review alluding to epilepsy, depression, exercise and lateralization.

Electroencephalograms (EEGs) reflect the electrical activity of the brain. Even when they are analyzed from healthy individuals, they manifest chaos in the nervous system. EEGs are likely to be produced by a nonlinear system, since a nonlinear system with at least 3 degrees of freedom (or state variables) may exhibit chaotic behavior. Furthermore, such systems can have multiple stable states governed by "chaotic" ("strange") attractors. A key feature of chaotic systems is the presence of an infinite number of unstable periodic fixed points, which are found in spontaneously active neuronal networks (e.g., epilepsy). The brain has chemicals called neurotransmitters that convey the information through the 10(16) synapses residing there. However, each of these neurotransmitters acts through various receptors and their numerous subtypes, thereby exhibiting complex interactions. Albeit in epilepsy the role of chaos and EEG findings are well proven, in another condition, i.e., depression, the role of chaos is slowly gaining ground. The multifarious roles of exercise, neurotransmitters and (cerebral) hemispheric lateralization, in the case of depression, are also being established. The common point of reference could be nonlinear dynamics. The purpose of this review is to study those nonlinear/chaotic interactions and point towards new theoretical models incorporating the oscillation caused by the same neurotransmitter acting on its different receptor subtypes. This may lead to a better understanding of brain neurodynamics in health and disease.

Brain↗

PubMed1997

Iterative function system and genetic algorithm-based EEG compression.

A method for EEG compression is proposed, using Iterative Function System (IFS) and Genetic Algorithms (GAs) with elitist model, keeping the quality sufficiently good for clinical purposes. Compression using IFS is usually called fractal compression. The self transformability property of the EEG signals is assumed and is exploited in the fractal compression technique. To ascertain the self transformability of the EEG signal, some isometric transformations have been applied. The technique described here utilizes Genetic Algorithm that decreases the search space for finding the self similarities in the given signal. This article presents theory and implementation of the proposed method. The fidelity of the reconstructed signal obtained by the present compression algorithm has been assessed both qualitatively and quantitatively. The compression ratios, for the EEG signals in various states, are found to be comparable to the other available techniques for EEG compression. In our method at least 85% data reduction has been achieved.

Algorithms↗

PubMed1997

Let your dreams come true through virtual reality.

Explore the source record for details and available documents.

Artificial Intelligence↗

PubMed1996

An MLP-based model for identifying qEEG in depression.

Manual differentiation of electroencephalography (EEG) paper recordings in cases of depression is not very helpful. So, a Multilayer Perceptron (MLP) has been used to differentiate the EEG power density spectra (qEEG) in the wakeful state from animals (control, exercised and depressed). The qEEG ranging from 1 to 30 Hz, at 1 Hz increments (30 input features) and also a slow, medium and fast activity (represented by three ranges of frequencies at the input) were used. After training with depressed and control qEEG only, the MLP has been found to distinguish successfully between the normal and the depressed rats in more than 80% of the cases, identifying, in the process, most of the exercised groups' EEG as normal. The reduction in the dimension of input features from 30 individual frequencies to 3 frequency bands has produced similar results. The rules generated for making such distinctions have been found to be similar to the clinical views.

Algorithms↗

PubMed1996

Chronic exercise alters EEG power spectra in an animal model of depression.

The EEG from frontal cortex, EMG and EOG were recorded from rats exposed to only exercise (Treadmill), only stress, exercise + stress and neither (control). In comparison with the control group, the percent of Delta activity in the awake was significantly increased in the depressed group and significantly decreased in the exercised groups, while for Beta-2, the reverse occurred; Theta increased and Beta-2 decreased in the NREM sleep state of the depressed group and the opposite happened for the exercised groups. Delta and Alpha-2 activity significantly increased in the depressed group, and they were significantly decreased in the exercised groups whereas the Beta-2 activity showed contrary changes in the REM sleep state. These findings indicate that exercise has the opposite effect from what stress has on qEEG and concomitant physical exercise reduces the effects of stress. Behavioral tests were done by Open Field (OF) and High Plus Maze (HPM). Slow EEG activity (Delta, Theta, Alpha) was significantly positively correlated with immobilization in the OF and defecation in both OF and HPM and negatively with the food intake, transfer latency in HPM; rearing, grooming and total ambulation in OF Whereas, fast activity (Beta-2) was significantly negatively correlated with immobilization in OF and defecation in OF and HPM, while positively with ambulation in the central squares of OF and time spent at the central cross and number of times arms crossed in the HPM.

Animals↗

PubMed1995

A neural network confirms that physical exercise reverses EEG changes in depressed rats.

The use of an artificial neural network (ANN) system to differentiate the EEG power density spectra in depressed from normal rats was tried. The beneficial effects of chronic physical exercise in reducing the effects of stress and therefore depression was also to be tested in animals by the same method. In this study, rats were divided into 4 groups, subjected to (i) chronic stress (D group); (ii) chronic exercise by treadmill running (EO group); (iii) exercise with stress (ES group) and (iv) handling (C group). The prefrontal cortical EEG, EMG and EOG were recorded simultaneously on paper and the digitized EEG signals were also stored in the hard-disk of a PC-AT through an ADC. After filtering the digitized signals, the EEG power spectra were calculated by an FFT routine. Three successive 4 s artefact-free epochs were averaged. The REM and NREM sleep periods as well as the awake period signals were analyzed separately. The FFT values from each of the 3 states, in the 4 groups of animals were tested by an ANN with 30 first layer neurons and a 2nd layer of a majority-vote-taker. The ANN could distinguish the depressed from the normal rats' EEG very well in REM (99%) sleep, NREM (95%) sleep and awake (81%) states. In most of the cases it identified the exercised rats' EEG as normal.

Animals↗

PubMed1995