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Biomedical subjects

C Eswaran

Publications and source records attributed to C Eswaran.

8 recordsLinked to original sources
PubMed2006

Context based error modeling for lossless compression of EEG signals using neural networks.

Two-stage lossless data compression methods involving predictors and encoders are well known. This paper discusses the application of context based error modeling techniques for neural network predictors used for the compression of EEG signals. Error modeling improves the performance of a compression algorithm by removing the statistical redundancy that exists among the error signals after the prediction stage. In this paper experiments are carried out by using human EEG signals recorded under various physiological conditions to evaluate the effect of context based error modeling in the EEG compression. It is found that the compression efficiency of the neural network based predictive techniques is significantly improved by using the error modeling schemes. It is shown that the bits per sample required for EEG compression with error modeling and entropy coding lie in the range of 2.92 to 6.62 which indicates a saving of 0.3 to 0.7 bits compared to the compression scheme without error modeling.

Data Compression↗
PubMed2006

Synthesis of audio spectra using a diffraction model.

It is shown that the intensity variations of an audio signal in the frequency domain can be obtained by using a mathematical function containing a series of weighted complex Bessel functions. With proper choice of values for two parameters, this function can transform an input spectrum of discrete frequencies of unit intensity into the known spectra of different musical instruments. Specific examples of musical instruments are considered for evaluating the performance of this method. It is found that this function yields musical spectra with a good degree of accuracy.

Acoustics↗
PubMed2005

Medical image compression using 3-D Hartley transform.

In this paper, 3-D discrete Hartley transform is applied for the compression of two medical modalities, namely, magnetic resonance images and X-ray angiograms and the performance results are compared with those of 3-D discrete cosine and Fourier transforms using the parameters such as PSNR and bit rate. It is shown that the 3-D discrete Hartley transform is better than the other two transforms for magnetic resonance brain images whereas for the X-ray angiograms, the 3-D discrete cosine transform is found to be superior.

Algorithms↗
PubMed2005

Artificial neural network based epileptic detection using time-domain and frequency-domain features.

Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.

Electroencephalography↗
PubMed1999

Automatic detection of intraoperative neurological injury.

Neurological injuries occurring during high-risk surgical procedures can be detected by monitoring intraoperative evoked potential signals. In this communication, an automatic injury detection algorithm is proposed in which the EP signal is modeled as a pole-zero filter and then the model parameters are applied as inputs to a classifier type neural network. A recognition rate of 96% is achieved using an experimental model of brain injury.

Algorithms↗
PubMed1997

An ECG classifier designed using modified decision based neural networks.

In this paper, a neural network based generalized software system is presented for automatic analysis of electrocardiograms (ECGs). The proposed system is capable of intuitively diagnosing the disease from the ECG using the knowledge acquired from the training. A modified decision based neural network which converges in a finite amount of time is employed. The training procedure used automatically varies the size of the network. The system is capable of being trained even without an expert's supervision. The physician can correct the network as and when a misclassification occurs, thus making the system less error-prone as time passes. The proposed system has been tested using an ECG data base representing different cardiological conditions such as bundle branch blocks and infarctions. The system is capable of detecting different types of arrhythmias also.

Algorithms↗
PubMed1997

Sequence data analysis reveals a relationship between LSR2, the recombinant fusion protein mimicing M. Leprae and VIF of bovine immunodeficiency virus (BIV).

In the course of computer simulation study looking for active sites for the interaction between MHC II and T7--a 12 residue long peptide of LSR2--a recombinant fusion protein mimicing the native bacillus M.Leprae--an interesting relationship between the antigenicity of LSR2 and VIF of BIF has come to light. Computer analysis study has revealed this stretch of residue from 36 to 48 of LSR2 is highly antigenic. The experimental observation seems to confirm the role of this 12 residue peptide in antibody response. In an effort to determine whether a significant sequence level relationship exists between this and any other known protein, the sequence homology of both protein and nucleic acid was studied. It is found that this 12 residue long peptide (T7) of LSR2 is homologous with Viral Infectivity Factor (VIF) of the Bovine Immunodeficiency Virus (BIV). Homology with translated nucleic acid sequence also indicate the same fact. The VIF gene which codes for this protein is known to be essential for ability of cell-free virus preparation to infect cells. These results lead to the question--whether this 12 residue long peptide which is common to both proteins play a role in their infectivity. Whether mutations in the peptide or elimination of this peptide from the protein and studying the effect of this on the diseases themselves may help in controlling them is another important question relevant to medical researchers.

Animals↗