Rakesh Kumar Sinha

Electro-encephalogram disturbances in different sleep-wake states following exposure to high environmental heat

In this study, cerebral electrical activity or electro-encephalogram (EEG) was studied following exposure to high environmental heat, in three different age groups of freely moving rats. Each age group was subdivided into three groups: the acute heat stress group, subjected to a single exposure of 4h at 38°C in the biological oxygen demand incubator; the chronic heat stress group, exposed for 21 days, for 1 h each day, at 38°C in the incubator; and the handling control group. The polygraphic sleep-wake recordings involved simultaneous recordings of cortical EEG, electrooculogram (EOG), and electromyogram (EMG), on paper and in digital form on computer hard disk, just after the heat exposure for the acute stressed rats and on the 22nd day for the chronic stressed rats. The power spectrum was calculated for 2s epochs of the EEG signals. Quantitative analyses of EEG (qEEG) showed that, in all three age groups, changes in higher-frequency components (β2) were significant in all sleep-wake states following both acute and chronic heat stress conditions. The power of β2 activity in all three age groups after acute heat exposure was significantly decreased during slow wave sleep (SWS) (p<0.05) and rapid eye movement sleep (p<0.05), whereas the reverse was observed in the awake state (p<0.05). Following chronic heat exposure, β2 activity was found to increase in all three sleep-wake stages in all groups of rats (p<0.01 for SWS in the weaning group and p<0.05 for other data). Thus the study demonstrated that the cortical EEG is sensitive to environmental heat, and alterations in EEG frequencies in different states of mental consciousness due to high heat can be differentiated efficiently by EEG power spectrum analysis.

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.

Low temperature synthesis of spinel (MgAl2O4)

Spinel (MgAl2O4) is prepared by a combined gelation-precipitation process from a mixed solution of sulphates of aluminium and magnesium. X-ray diffraction of gel samples calcined at different temperatures shows that a spinel-like phase starts forming at 600 °C and complete conversion is obtained at 1000 °C in 1 h. The average particle size of the calcined powder is 0.2 μm. Pressed pellets have good sinterability even at low temperatures (1100 °C to 1450 °C). At 1450 °C, 95% of the theoretical density is achieved within 3 h. Scanning electron micrographs reveal faceted grains and fine grain size.

Artificial neural network detects changes in electro-encephalogram power spectrum of different sleep-wake states in an animal model of heat stress

An effective application is presented of a back-propagation artificial neural network (ANN) in differentiating electro-encephalogram (EEG) power spectra of stressed and normal rats in three sleep-wakefulness stages. The rats were divided into three groups, one subjected to acute heat stress, one subjected to chronic heat stress and one a handling control group. The polygraphic sleep recordings were performed by simultaneous recording of cortical EEG, electro-oculogram (EOG) and electromyogram (EMG) on paper and in digital form on a computer hard disk. The preprocessed EEG signals (after removal of DC components and reduction of base-line movement) were fragmented into 2s artifact-free epochs for the calculation of power spectra. The slow-wave sleep (SWS), rapid eye movement (REM) sleep and awake (AWA) states were analysed separately. The power spectrum data for all three sleep-wake states in the three groups of rats were tested by a back-propagation ANN. The network contained 60 nodes in the input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in the hidden layer and an output node. The ANN was found effective in differentiating the EEG power spectra from stressed to normal spectral patterns following acute (92% in SWS, 85.5% in REM sleep, 91% in AWA state) as well as chronic heat exposure (95.5% in SWS, 93.8% in REM sleep, 98.5% in AWA state).

Mu and beta rhythm modulations in motor imagery related post-stroke EEG: a study under BCI framework for post-stroke rehabilitation

Motor impairment after stroke is a leading cause of disability. Fortunately there is sufficient evidence that undertaking motor imagery (MI) in conjunction with physical practice of rehabilitation tasks leads to enhanced functional recovery of paralyzed limbs among stroke sufferers. This requires ensuring patient engagement through neuro-feedback which can be provided by an MI based brain-computer interface (BCI). A BCI uses EEG from motor cortex and finds MI related modulation of sensorimotor rhythms, as it is known that left or right hand MI by a healthy subject results in a desynchronization (ERD) of mu (8-13Hz) in contralateral EEG along with synchronization (ERS) of beta rhythm (18-24Hz) in ipsilateral EEG [1]. This study examines if the MI related EEG from hemiplegic patients has similar mu and beta oscillation patterns and its effectiveness in BCI development. Under the supervision of rehabilitation experts 3 left and 2 right hemiplegic stroke patients (59±12y) underwent up to 12 EEG recording sessions (each session has 120 trials of 7 seconds (including 3s rest)). The subjects were performing MI while playing a ball-basket game as part of the neuro-feedback. During trials, two bi-polar channels C3 and C4 and left/right imagination labels were recorded for processing. The EEG power in the impaired hemisphere was found much lower than that of the healthy side. Off-line analysis using widely-used power spectral density (PSD) features and a linear discriminant analysis (LDA) classifier resulted in poor MI classification accuracy (CA) for impaired limb. We therefore, use bispectrum-based (BSP) feature extraction technique along with LDA classifier. BSP computes the sum of absolute log-bispectrum of band-passed EEG, which finds non-Gaussian and nonlinear properties of the signal providing bispectral energy. Two BSP features from each channel of EEG were extracted. A 5-fold cross validation technique followed by optimization was applied to find the best classifier for the subject-specific BCI system. Feature CA obtained in the optimization phase (Table ​(Table1)1) is computed from the inter-session mean of maximum CA. The CA was grouped in terms of selected frequency band (used in BSP). Fig. ​Fig.11 displays an example of accuracy distribution (observed for the subject P1) during the time course of the paradigm. Table 1 Two class MI classification accuracy for stroke subjects with different frequency bands Figure 1 CA during the time course of paradigm (P1)

Chronic non-thermal exposure of modulated 2450 MHz microwave radiation alters thyroid hormones and behavior of male rats.

Purpose: The purpose of this investigation was to analyze the effects of leakage microwave (2450 MHz) irradiation on thyroid hormones and behavior of male rats. Materials and methods: Experiments were carried out on two groups of male rats (exposure and control, respectively). Radio-immuno assay (RIA) methods were used for estimation of 3,5,3′-triiodothyronine (T3), thyroxine (T4) and thyrotrophin or thyroid stimulating hormone (TSH). The assessments of behavioral changes were performed in Open-Field (OF) and Elevated Plus-Maze (EPM) apparatuses. Results: Following chronic microwave exposure, rats were found hyperactive and aggressive on the 16th and 21st days. Behavioral changes in OF were analyzed and found to be significantly changed from controls (p < 0.05) for immobilization, rearing and ambulation behavior. In EPM, rats showed increased activity with decreased time spent in the open arm and more time spent in the center on the 11th (p < 0.05), 16th (p < 0.05) and 21st day (p < 0.01) after irradiation. Changes in behavioral parameters are also correlated with the trend of changes, compared to control animals, in hormonal blood levels of T3 (decreased on the 16th day, p < 0.05 and 21st day, p < 0.01) and T4 (increased on the 21st day, p < 0.05). Conclusion: Low energy microwave irradiation may be harmful as it is sufficient to alter the levels of thyroid hormones as well as the emotional reactivity of the irradiated compared to control animals.

Sleep–wake study in an animal model of acute and chronic heat stress

The study of the variations in different parameters of sleep-wake states following exposure to high environmental heat in three different age groups of freely moving rats have been presented in this paper. Each age group of rats was subdivided in three group (i) acute heat stress--subjected to a single exposure for 4 h in the BOD (Biological Oxygen Demand) incubator at 38 degrees C; (ii) chronic heat stress--exposed for 21 days daily for 1 h in the incubator at 38 degrees C, and (iii) handling control groups. The polygraphic, analog as well as digital sleep-wake recordings were performed just after the heat exposure from acute stressed rats and on 22nd day from chronic stressed rats. The results of this study revealed that acute exposure to high environmental heat increases sleep efficiency with significant increase in SWS (slow wave sleep) decrease in AWA (awake) time in all three age groups of rats. The increase in SWS and the sleep efficiency in these groups of rats at the cost of decreased time of AWA, indicates the involvement of the hypothalamus in thermoregulatory mechanism to conserve the energy of the body following sudden exposure to high heat. However, the reverse results were observed in the chronic stressed groups of rats, which have occurred mostly owing to the adaptations of the brain functions due to repetitive exposure to environmental heat. In consequence, the present study exhibits that the sleep is highly susceptible to the environmental heat and it is sensitive to the intensity, duration and the mode of exposure.

Jaya Based ANFIS for Monitoring of Two Class Motor Imagery Task

The brain–computer interface (BCI) identifies brain patterns to translate thoughts into action. The identification relies on the performance of the classifier. In this paper, identification and monitoring of electroencephalogram-based BCI for motor imagery (MI) task is proposed by an efficient adaptive neuro-fuzzy classifier (NFC). The Jaya optimization algorithm is integrated with adaptive neuro-fuzzy inference systems to enhance classification accuracy. The linguistic hedge (LH) is used for proper elicitation and pruning of the fuzzy rules and network is trained using scaled conjugate gradient (SCG) and speeding up SCG (SSCG) techniques. In this paper, Jaya-based k-means is applied to divide the feature set into two mutually exclusive clusters and fire the fuzzy rule. The performance of the proposed classifier, Jaya-based NFC using SSCG as training algorithm and is powered by LH (JayaNFCSSCGLH), is compared with four different NFCs for classifying two class MI-based tasks. We observed a shortening of computation time per iteration by 57.78% in the case of SSCG as compared with the SCG technique of training. LH-based feature selecting capability of the proposed classifier not only reduces computation time but also improves the accuracy by discarding irrelevant features. Lesser computation time with fast convergence and high accuracy among considered NFCs make it a suitable choice for the real-time application. Supremacy of JayaNFCSSCGLH among the considered classifier is validated through Friedman test. Classification result is used to control switching of light emitting diode, turning thoughts into action.

Classification of two class motor imagery tasks using hybrid GA-PSO based k -means clustering

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

On fusion of heart and brain signals for hybrid BCI

This paper investigates the fusion of ECG with EEG in devising a hybrid brain-computer interface (hBCI). Effortful motor imagery (MI) based BCI experiments were arranged with a twelve seconds of cue-based MI paradigm on six healthy individuals over two sessions of 160 trials, while ECG and EEG signals were simultaneously recorded. The proposed hBCI uses bispectrum based features from EEG and ECG along with an LDA classifier. The off-line analysis shows an improvement in MI task detection accuracy if both ECG and EEG features are considered together. In addition, the time domain analysis of ECG signal shows that the average heart rate increases during MI state, which clearly shows that the cardiac system responds to MI related tasks.