Barda Nand Das

Backpropagation Artificial Neural Network Classifier to Detect Changes in Heart Sound due to Mitral Valve Regurgitation

The phonocardiograph (PCG) can provide a non-invasive diagnostic ability to the clinicians and technicians to compare the heart acoustic signal obtained from normal and that of pathological heart (cardiac patient). This instrument was connected to the computer through the analog to digital (A/D) converter. The digital data stored for the normal and diseased (mitral valve regurgitation) heart in the computer were decomposed through the Coifman 4th order wavelet kernel. The decomposed phonocardiographic (PCG) data were tested by backpropagation artificial neural network (ANN). The network was containing 64 nodes in the input layer, weighted from the decomposed components of the PCG in the input layer, 16 nodes in the hidden layer and an output node. The ANN was found effective in differentiating the wavelet components of the PCG from mitral valve regurgitation confirmed person (93%) to normal subjects (98%) with an overall performance of 95.5%. This system can also be used to detect the defects in cardiac valves especially, and other several cardiac disorders in general.

Prediction of Heat-Illness Symptoms with the Prediction of Human Vascular Response in Hot Environment Under Resting Condition

The thermoregulatory control of human skin blood flow is vital to maintain the body heat storage during challenges of thermal homeostasis under heat stress. Whenever thermal homeostasis disturbed, the heat load exceeds heat dissipation capacity, which alters the cutaneous vascular responses along with other body physiological variables. Whole body skin blood flow has been calculated from the forearm blood flow. Present model has been designed using electronics circuit simulator (Multisim 8.0, National Instruments, USA), is to execute a series of predictive equations for early prediction of physiological parameters of young nude subjects during resting condition at various level of dry heat stress under almost still air to avoid causalities associated with hot environmental. The users can execute the model by changing the environmental temperature in °C and exposure time in minutes. The model would be able to predict and detect the changes in human vascular responses along with other physiological parameters and from this predicted values heat related-illness symptoms can be inferred.

Backpropagation ANN-Based Prediction of Exertional Heat Illness

Exertional heat illness is primarily a multi-system disorder results from the combined effect of exertional and thermoregulation stress. The severity of exertional heat illness can be classified as mild, intermediate and severe from non-specific symptoms like thirst, myalgia, poor concentration, hysteria, vomiting, weakness, cramps, impaired judgement, headache, diarrhea, fatigue, hyperventilation, anxiety, and nausea to more severe symptoms like exertional dehydration, heat cramps, heat exhaustion, heat injury, heatstroke, rhabdomyolysis, and acute renal failure. At its early stage, it is quite difficult to find out the severity of disease with manual screening because of overlapping of symptoms. Therefore, one need to classify automatically the disease based on symptoms. The 7:10:1 backpropagation artificial neural network model has been used to predict the clinical outcome from the symptoms that are routinely available to clinicians. The model has found to be effective in differentiating the different stages of exertional heat-illness with an overall performance of 100%.

DFAspike: A new computational proposition for efficient recognition of epileptic spike in EEG

An automated method has been presented for the detection of epileptic spikes in the electroencephalogram (EEG) using a deterministic finite automata (DFA) and has been named as DFAspike. EEG data (sampled, 256 Hz) files are the inputs to the DFAspike. The DFAspike was tested with different data files containing epileptic spikes. The obtained recognition rate of epileptic spike was 99.13% on an average. This system does not require any kind of prior training or human intrusion. The result shows that the designed system can be very effectively used for the detection of spikes present in the recorded EEG signals.

Digital-analog hybrid control model for eukaryotic heat shock response illustrating the dynamics of heat shock protein 70 on exposure to thermal stress

We are introducing in this paper a digital-analog hybrid model approach for the study of a complete gene regulatory network; the heat shock response (HSR) network of eukaryotes. HSR is a crucial and widely studied cellular phenomenon occurring due to various stresses on the cell, and is characterised by the induction of heat shock genes resulting in the production of heat shock proteins (HSPs) which restores cellular homeostasis by maintaining protein integrity. We are proposing a model which incorporates simple digital and analog components which mimic the functioning of biological molecules involved in HSR and model their dynamics and behaviour. The simulation result of the circuit for the production of HSP70 has been found to be consistent with published experimental results. The qualitative behaviour of the HSR is expressed through a truth table. Through this novel approach, the authors have tried to develop a level of understanding of the interactions of the parts of the HSR system and of this system as a whole.

Computer Simulation of Heat Transfer in Different Tissue Layers of Body Extremities Under Heat Stress in Deep Anesthetic Condition

Many mathematical models of thermoregulation in humans have been developed, so far. These models appeared to be very useful tools for studying temperature regulation in humans under adverse environmental conditions. However, no one discussed the heat transfer characteristics of denervated subjects. Thus, the present study is concerned with aspects of the passive system for denervated subjects: (1) modeling the human body extremities (2) modeling heat transport mechanism within the body and at its periphery. The present model was simulated using the software (Wintherm 8.0, Thermoanalytics, USA) for different body segments to predict the heat flow between body core and skin surface with changes in environmental temperature with fixed relative humidity and wind velocity. The simulated model for comparative study of internal temperature distribution of hand, arm, leg and feet segments yielded remarkably good results and observed to be in trends with previously cited work under ambient environmental condition and at controlled room temperature. Models could be used to measure the temperature distribution in human limbs during local hyperthermia and to investigate the interaction between limbs and the thermal environment.

Mechanistic electronic model to simulate and predict the effect of heat stress on the functional genomics of HO-1 system: Vasodilation

The present work is concerned to model the molecular signalling pathway for vasodilation and to predict the resting young human forearm blood flow under heat stress. The mechanistic electronic modelling technique has been designed and implemented using MULTISIM 8.0 and an assumption of 1V/ degrees C for prediction of forearm blood flow and the digital logic has been used to design the molecular signalling pathway for vasodilation. The minimum forearm blood flow has been observed at 35 degrees C (0 ml 100 ml(-1)min(-1)) and the maximum at 42 degrees C (18.7 ml 100 ml(-1)min(-1)) environmental temperature with respect to the base value of 2 ml 100 ml(-1)min(-1). This model may also enable to identify many therapeutic targets that can be used in the treatment of inflammations and disorders due to heat-related illnesses.