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Diabetes is a very common occurring disease, diagnosed by hyperglycemia. The established mode of diagnosis is the analysis of blood glucose level with the help of a hand-held glucometer. Nowadays, it is also known for affecting multi-organ functions, particularly the microvasculature of the cardiovascular system. In this work, an alternative diagnostic system based on the heart rate variability (HRV) analysis and artificial neural network (ANN) and support vector machine (SVM) have been proposed. The experiment and data recording has been performed on male Wister rats of 10–12 week of age and 200 ± 20 gm of weight. The digital lead-I electrocardiogram (ECG) data are recorded from control (n = 5) and Streptozotocin-induced diabetic rats (n = 5). Nine time-domain linear HRV parameters are computed from 60 s of ECG data epochs and used for the training and testing of backpropagation ANN and SVM. Total 526 (334 Control and 192 diabetics) such datasets are computed for the testing of ANN for the identification of the diabetic conditions. The ANN has been optimized for architecture 9:5:1 (Input: hidden: output neurons, respectively) with the optimized learning rate parameter at 0.02. With this network, a very good classification accuracy of 96.2% is achieved. While similar accuracy of 95.2% is attained using SVM. Owing to the successful implementation of HRV parameters based automated classifiers for diabetic conditions, a non-invasive, ECG based online prognostic system can be developed for accurate and non-invasive prediction of the diabetic condition.

It is a well-established fact that patients of Covid 19 have musculoskeletal dysfunctions in addition to cardiorespiratory and psychosocial problems. Musculoskeletal system is amongst the most common systems found to be affected in patients admitted in ICU for prolonged periods and some patients of Covid 19 do have to spend a reasonably long period in ICU’s. Prolonged hospital stay may be needed in Covid 19 patients having ARDS or pneumonia and symptoms of fever, cold and cough to monitor their recovery. This may result in musculoskeletal dysfunctions as demonstrated in some studies. Thus, treating the musculoskeletal system becomes a vital part of the regimen to be followed during the patients stay in the ICU which needs to be followed up even after the patient is shifted from the ICU by keeping a track of Physical Functional performance of the subjects after discharge. This experimental study was done on 150 Covid-19 positive subjects to see the long term effectiveness of a structured exercise protocol on the Physical Functional performance of Covid 19 patients. The results of the study were analysed statistically and both the outcome measures FIMS and POMA showed significant changes with p value

Diabetes mellitus (DM) is a multifactorial disease characterized by hyperglycemia. The type 1 and type 2 DM are two different conditions with insulin deficiency and insulin resistance, respectively. It may cause atherosclerosis, stroke, myocardial infarction and other relevant complications. It also features neurological degeneration with autonomic dysfunction to meet metabolic demand. The autonomic balance controls the physiological variables that exhibit nonlinear dynamics. Thus, in current work, nonlinear heart rate variability (HRV) parameters in prognosis of diabetes using artificial neural network (ANN) and support vector machine (SVM) have been demonstrated. The digital lead-I electrocardiogram (ECG) was recorded from male Wister rats of 10–12 week of age and 200 ± 20 gm of weight from control (n = 5) as well as from Streptozotocin induced diabetic rats (n = 5). A total of 526 datasets were computed from the recorded ECG data for evaluating thirteen nonlinear HRV parameters and used for training and testing of ANN. Using these parameters as inputs, the classification accuracy of 86.3% was obtained with an ANN architecture (13:7:1) at learning rate of 0.01. While relatively better accuracy of 90.5% was observed with SVM to differentiate the diabetic and control subjects. The obtained results suggested that nonlinear HRV parameters show distinct changes due to diabetes and hence along with machine learning tools, these can be used for development of noninvasive low-cost real-time prognostic system in predicting diabetes using machine learning techniques.

Plants consistently synthesize and accumulate medically valuable secondary metabolites which can be isolated and clinically tested under in vitro conditions. An advancement with such important phytochemical production has been recognized and utilized as herbal drugs. Bioactive andrographolide (AGL; C20H30O5) isolated from Andrographis paniculate (AP) (Kalmegh) is a diterpenoid lactones having multifunctional medicinal properties including anti-manic, anti-inflammatory, liver, and lung protective. AGL is known for its immunostimulant activity against a variety of microbial infections thereby, regulating classical and alternative macrophage activation, Ag-specific antibody production during immune disorder therapy. In vitro studies with AGL found it to be effective against multiple tumors, neuronal disorders, diabetes, pneumonia, fibrosis, and other diverse therapeutic misadventures. Generally, virus-based diseases like ZIKA, influenza A virus subtype (H1NI), Ebola (EBOV), Dengue (DENV), and coronavirus (COVID-19) epidemics have greatly increased scientific interest and demands to develop more effective and economical immunomodulating drugs with minimal side effects. Trials and in vitro pharmacological studies with AGL and medicinally beneficial herbs might contribute to benefit the human population without using chemical-based synthetic drugs. In this review, we have discussed the possible role of AGL as a promising herbal-chemo remedy during human diseases, viral infections and as an immunity booster.

Breast Cancer (BC) is the leading cause of death in women, worldwide. The Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) of BC can be studied using HRV measures. The main purpose of this chapter is to give an insight to clinicians via HRV measures with respect to age to make them understand the PS of patients. Data from 114 BC patients was segregated into two age groups, G1 (20 to 40 years) and G2 (41 to 75 years). The 5-minute electrocardiogram of the subjects was taken and HRV measures were extracted. One-way ANOVA with Posthoc Tukeys' HSD test was done. Triangular Index, Ratio of standard deviation of poincare plot perpendicular to the line of identity to the standard deviation along line of identity, Detrended Fluctuation Analysis descriptors, Approximate Entropy, Sample Entropy and Correlation Dimension significantly decreased from ECOG0 to 4 and from G1 to G2. The sympathetic activity increased with vagal withdrawal as age advanced.

Lung cancer mortality is increasing nowadays globally. The objective of the study was to analyze the improvement in performance status (PS) of lung cancer patients using heart rate variability (HRV) measures after the treatment. The electrocardiogram (ECG) of 104 consecutive lung cancer subjects and 30 healthy controls was recorded for 5 min. The R to R (RR) interval plot was extracted from ECG using Acknowledge 4.0 software, which was fed to Kubios HRV 2.1, Finland, for HRV analysis. R Statistical Software v.3.3.2, 64 bit, was utilized for statistical analysis. The HRV measures increased after the treatment. Considering gender as confounder, it was found that male subjects had higher values than females and it increased after the treatment. Control males had lower values of all HRV measures than control females. Increased HRV measures after the treatment indicate parasympathetic dominance and improvisation in the quality of PS of patients.

Mental calculation demanding tasks cause changes in the autonomic modulation of the cardiac function. Heart rate variability (HRV) is a parameter to understand the autonomic changes. To examine HRV in young individuals in the performance of two levels of arithmetic mental calculation tasks. We assessed 22 participants (18–25 years; both male and female) who performed two levels of mental arithmetic calculations, i.e., 15 easy and 15 calculative. Each task was for 10 s, for both easy and calculative and presented in random manner, through Superlab 4.5 (Cedrus Inc.) and electrocardiogram data were acquired by MP35 (Biopac Inc., Goleta, California, USA). Acqknowledge 4.1 and KUBIOS 2.1 software were used for HRV analysis. Decreased heart rate in calculative task as compared to easy task (p

Brain-computer interface (BCI) technology primarily helps the physically challenged people get in touch with the outside world using the brain signals. Because of the simplicity of the electroencephalogram-based BCI methods, a number of researchers have shown interest and have done a lot of work in this field. However, for a common man, to understand the concept of BCI, the article presents a fundamental model with a brief summary of the various steps involved in the process of the whole system. Finally, some research work done to date has also been discussed. The whole article will help a person to understand the basic concepts of the electroencephalogram-based BCI system going through the model to the discussions.

Brain-computer interface (BCI) systems are used to establish communication pathways between the brain signals and a peripheral device. One such pathway can be fashioned between these signals and an electric drive to be further used for any neuro-aid application. Brain signals, which in this study are the electroencephalogram (EEG) signals from a specified focal area, get synchronized and desynchronized upon the occurrence of different motor imagery (MI). Some specific features of the neuronal brain activities occurring during MI can be extracted to get the relevant information from them. This paper proposes an algorithm based on time-varying signed distance (TSD) to obtain well-separated EEG data for the process of feature extraction. Post TSD application, power spectral density (PSD) based features were extracted, followed by using the measure of cross-correlation to select the most dominant PSD based features. A quadratic discriminant analysis (QDA) is adopted to classify the test data and to generate further control signals for regulating the motion of brushless DC (BLDC) motor. The rotation of the motor is governed by the different classes of MI data. For the experimental dataset, the accuracy of the classifier was found to be 83.3 %, 71.6 %, and 78.3 %, respectively, for three different subjects. The case corroborates the efficacy of the proposed algorithm for practical applications. Experimental validation of the proposed system is presented on a 1 kW brushless DC (BLDC) motor drive system.

The paper proposes a novel methodology of de-noising raw electroencephalogram (EEG) data from ocular artifacts (OAs) and alpha waves extraction from motor imagery-based signals that could be further utilized for brain–computer interface (BCI)-based applications. An algorithm based on discrete wavelet transform (DWT) and nonlinear adaptive filtering for the removal of OA is advocated, with an aim of making the process computationally intelligent. This algorithm has been tested on pre-recorded EEG dataset for BCI (Dataset IIIa; obtained from the website of the BCI Competition III). To further validate the competence of the proposed method, synthetic EEG signals were created, which were fused with white Gaussian noise. A total of 20 EEG signals were generated, half of which had added noise with a signal-to-noise ratio (SNR) of 10[Formula: see text]dB and other half had added noise of 5 dBSNR. Each signal contained 1000 samples with a sampling frequency of 250[Formula: see text]Hz. An optimum bandpass filter (FIR and IIR) for extraction of alpha waves has been suggested. FIR Equiripple filter is found most appropriate for the task as it has highest SNR and computes the response faster when compared with other filters. Among different mother wavelets, Daubechies 4 wavelet obtained using statistical thresholding denoises the EEG data most successfully. Correlation and root mean square error (RMSE) parameters show that the performance of nonlinear adaptive filter developed using nonlinear Volterra series has an edge over conventional adaptive filters for the intended purpose.

Andrographolide is a diterpene secondary metabolite product of Andrographis paniculata. It has been known to be a pharmaceutically important compound synthesized via the cytosolic mevalonate (MVA) and the plastidial 2-C-methyl-d-erythritol-4-phosphate (MEP) pathways. To understand the biosynthetic pathway of andrographolide biosynthesis in Andrographis paniculata, lovastatin, fosmidomycin and methyl jasmonate (MeJA) were used to inhibit the key enzymes 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR), and 1-deoxy-d-xylulose-5-phosphate reducto-isomerase (DXR) involved in the synthesis of andrographolide in the MVA and MEP pathways, respectively. The inhibition of andrographolide accumulation was linked with the expression level of the studied regulatory genes, 3-hydroxy-3-methyl glutaryl coenzyme A synthase (hmgs), 3-hydroxy-3-methyl glutaryl coenzyme A reductase (hmgr), 1-deoxyxylulose-5-phosphate synthase (dxs), 1-deoxyxylulose-5-phosphate reductoisomerase (dxr), 1-hydroxy-2-methyl-2-(E)-butenyl-4-diphosphate synthase (hds),1-hydroxy-2-methyl-2-(E)-butenyl-4-diphosphate reductase (hdr), 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate reductase(isph), isopentenyl diphosphate isomerase (ipp), geranylgeranyl diphosphatesynthase (ggps) of the MVA and MEP pathways. The pathways associated transcript expression level, and andrographolide biosynthesis was significantly modulated by the inhibitors indicating that the andrographolide biosynthesis is strongly responsive at the transcriptional level. The results demonstrated that both pathways can contribute to the biosynthesis of andrographolide in A. paniculata. Both hmgr and dxr played a critical role consistent with some crossover between MVA and MEP pathways in andrographolide biosynthesis.

This paper explores the changes in power associated with the imagination of motor imagery (MI) signals corresponding to hand movement and foot movement. The study presented in this paper establishes a correlation between MI signals and Event-RelatedSynchronization (ERS)and Event-Related Desynchronization (ERD) phenomenon with the help of Band power features. This fact has been further validated with the help of Linear Discriminant Analysis (LDA) Classifier. The analysis is conducted on dataset IIIa, obtained from BCI Competition III conducted by Graz University, Austria. The dataset is a multiclass data recorded for three subjects. The accuracy of individual classes and inter-class is calculated. The average accuracy for subject K3b, K6b and L1b obtained is 91.92%, 59.97%, 57.89% respectively when training data is used as test data and when a set of test data is used the resultant accuracy obtained is 85.66%, 65.33%, 58% for the three subjects respectively. Foot data showed very less accuracy in comparison with the data of hands. It is analyzed through the studies conducted in this paper that simple phenomenon of ERD/ERS can be utilized in a multiclass Brain-Computer Interface (BCI) system operated for MI.

Functional damage of mitochondria and chloroplasts under stress contributes to reactive oxygen species (ROS) induced cell death. A high percentage of cell mortality during microspore culture is a limiting factor in haploid and doubled haploid plant production. In the present study, we studied the contribution of dimethyl tyrosine (DMT) conjugated short peptides to microspore embryogenesis. These DMT-peptides, which are known to translocate to subcellular targets and scavenge ROS, mitigate the effect oxidative stress plays on microspore viability and embryogenesis. The number of viable microspores was significantly higher in the presence of SS-31 and caspase-3-inhibitor (Ac-DEVD-CHO). In particular, the total number of green plant regeneration was increased by 42 % in the presence of SS-02, and by 55 % in the presence of SS-31, in triticale. Conversely, lower caspase-3-like activities were observed in the presence of SS-31 and Ac-DEVD-CHO, and intracellular ROS was reduced in the presence of SS-31, supporting the involvement of SS-31 in reducing microspore cell death by mitigating ROS and caspase-3-like activity. This study further supports the concept that antioxidant conjugated peptides offer a useful strategy for reducing ROS in plant cells.

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the classification of alcoholism in the central nervous system. The EEG signals for chronic alcoholic conditions taken from motor cortex region were divided into five sub-frequency band and Hilbert Huang Transform is applied for feature extraction of the EEG signals. Since the extracted feature has large data dimension, it is reduced using Linear Discriminate Analysis. Support Vector Machine has been used for classification. Highest Classification accuracy 76.67% has been seen in the central (CZ) and left central (C3) and left parietal (P3) hemisphere when the classifier was tested with 150 EEG epochs of two seconds. In present results CZ, C3, P3 focal area of brain shown the better classification accuracy, which can be concludes as the persons with chronic alcoholism are having hyper active zone in comparison with control. Considering the classification accuracy obtained by SVM clustering with mean band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive EEG signals can be considered as a tool for development of an automated online diagnostic system for the chronic alcoholic condition.

Microspore cell death and low green plant production efficiency are an integral obstacle in the development of doubled haploid production in wheat. The aim of the current study was to determine the effect of anti-apoptotic recombinant human B-cell lymphoma-2 (Bcl-2△21) and caspase-3-inhibitor (Ac-DEVD-CHO) in microspore cell death in bread wheat cultivars AC Fielder and AC Andrew. Induction medium containing Bcl-2△21 and Ac-DEVD-CHO yielded a significantly higher number of viable microspores, embryo-like structures and total green plants in wheat cultivars AC Fielder and AC Andrew. Total peroxidase activity was lower in Bcl-2△21 treated microspore cultures at 96 h of treatment compared to control and Ac-DEVD-CHO. Electron paramagnetic resonance study of total microspore protein showed a different scavenging activity for Bcl-2△21 and Ac-DEVD-CHO. Bcl-2△21 scavenged approximately 50% hydroxyl radical (HO•) formed, whereas Ac-DEVD-CHO scavenged approximately 20% of HO•. Conversely, reduced caspase-3-like activities were detected in the presence of Bcl-2△21 and Ac-DEVD-CHO, supporting the involvement of Bcl-2△21 and Ac-DEVD-CHO in increasing microspore viability by reducing oxidative stress and caspase-3-like activity. Our results indicate that Bcl-2△21 and Ac-DEVD-CHO protects cells from cell death following different pathways. Bcl-2△21 prevents cell damage by detoxifying HO• and suppressing caspase-3-like activity, while Ac-DEVD-CHO inhibits the cell death pathways by modulating caspase-like activity.

The application of Brain Computer Interface (BCI) for rehabilitation purpose has gained wide popularity in recent times. BCI for rehabilitation involves detection of brain signals, when the subject performs some sort of Motor Imagery (MI) task, for example, imagination of movement of limbs. Imagination of such movement causes desynchronization of neurons of one part of the brain gets within other parts synchronized. Band power features are best suited for quantification of the synchronization phenomenon. In the present work, extreme learning machine (ELM) and support vector machine (SVM) based classifiers are used to classify the test data. The classifier output is further used to generate control signals for driving a stepper motor, which may be used to drive some neuro-aid application device. In order to achieve a workable model for pragmatic applications, it is necessary to design a robust in nature stepper motor. Open loop analysis, closed loop analysis and performance analysis of motor with possible disturbances are carried out to evaluate the effectiveness of the proposed work. The maximum accuracy using ELM and SVM classifiers are achieved as 90% and 87.78% with a training time of 0.2496[Formula: see text]s and 3.964[Formula: see text]s, respectively. In the open loop and closed loop analysis, the desired angular movement (task imagined for rehabilitation) is achieved with an accuracy of 54.14% and 93.4%, respectively. These results suggest that a BCI system can be designed with higher efficiency with the help of MI data.

The brain signal or Electroencephalogram (EEG) has been proved as one of the most important bio-signal that deals with a number of problems and disorders related to the human being. Epilepsy is one of the most commonly known disorders found in humans. The application of EEG in epilepsy related research and treatment is now a very common practice. Variety of smart tools and algorithms exist to assist the experts in taking decision related to the treatment to be provided to an epileptic patient. However, web based applications or tools are still needed that can assist those doctors and experts, who are not having such existing smart tools for EEG analysis with them. In the current work, a web based system named WEBspike has been proposed that breaks the geographical boundary in assisting doctors in taking proper and fast decision regarding the treatment of epileptic patient. The proposed system receives the EEG data from various users through internet and processes it for Epileptic Spike (ES) patterns present in it. It sends back a report to the user regarding the appearance of ES pattern present in the submitted EEG data. The average spike recognition rate obtained by the system with the test files, was 99.09% on an average.

The primary focus of this article was to review the current understanding of motor imagery (MI) as a cognitive process and thereby explore correlated biosignals for devising advanced brain-computer interfaces (BCIs) helping toward overcoming the limitations of current systems.To this end, the article first outlines the process of performing MI, ensuring kinesthetic experiences, and explores the literature to ascertain main brain areas that are activated during MI. Mental chronometry as a conventional method of assessing MI is then discussed, and its main limitation of not being able to measure MI vividness is highlighted. Neurophysiology and neuroelectrophysiology of MI are then analyzed to explain the phenomena of event-related desynchronization and event-related synchronization occurring in the sensorimotor rhythms, used as the principal feature in electroencephalogram-based BCIs.The current understanding on the effect of MI on regional cerebral blood flow and neural metabolism is then reviewed with the objective of explaining how functional magnetic resonance imaging and near infrared spectroscopy can be used to devise a metabolic BCI system.The literature is then reviewed to explore the current understanding on the effect of MI on peripheral nervous system causing variations in autonomic responses. Most importantly, the review identifies a range of biosignals including oxygen consumption, respiratory rate, heart rate, and skin resistance, which have strong potential for developing enhanced BCI devices either alone or in combination with other signals including electroencephalogram and near-infrared spectroscopy, through multisensor integration.

Manual processing of recorded EEG data for characteristics like epileptic spikes is very time consuming since the recording of EEG for a longer duration producing enormous amount of data. Therefore, automated systems are required to speed up the processing. In the current work, a classificat ion method has been proposed for detecting the epileptic spikes in the recorded EEG data by using Linear Discriminant Analysis (LDA) and has been named LDASpike. The prerecorded EEG data files were used as input to LDASpike and the output produced was the total number of spikes present in each EEG file. The proposed method results on an average sensitivity 100% and selectivity 95.38%, when the train ing and testing data were same. Ho wever, with four fo ld cross- validation applied in this work, the sensitivity and selectivity were achieved as 98.45% and 96.06%, respectively, on an average. Though a little time init ially is spent to train the system but the result produced by the system is very promising and can be compared with the existing standard methods. This system can also works with the real time recording and processing for a clinical setup.

Need of an alternative method for communication has been seriously felt for man-machine interface (MMI) because of difficulties in the analysis of complex electroencephalogram (EEG). The proposed method analyses the alterations in autonomic responses due to prolonged eye gaze. The experimental paradigm was designed to include 20 trials of 30 s for eye gaze and 10 s for relaxation. Along with electrooculogram (EOG), electrocardiogram (ECG), pulse plethysmogram (PPG) and electrodermal activity (EDA) was recorded from the five male subjects. Results demonstrated that the eye gaze modulates heart rate, pulse rate and EDA signals that were too analyzed to occur with the latency of nearly 5 s, which is nearer to the EEG based brain-machine interfaces (BMI). The alterations in autonomic variables persist for longer duration and the maximum change in pulse rate was observed at 26.79 s (5.16 beats/minute) in comparison to the maximum change of heart rate (6.27 beats/minute) at 27.45 s, respectively. Further, the changes in EDA were found more at the onset of events. With the above findings, it can be suggested that the changes in autonomic responses with the mental effort produced by eye gaze were distinct and provides a good platform for the development of MMI.

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.

Self-Organising Feature Map (SOFM) along with learning vector quantizers (LVQ) have been designed to identify the alterations in brain electrical potentials due to exposure to high environmental heat in rats. Three groups of rats were considered—acute heat stressed, chronic heat stressed and control groups. After long EEG recordings following heat exposure, EEG data representing three different vigilance states such as slow wave sleep (SWS), rapid eye movement (REM) sleep and AWAKE were visually selected and further subdivided into 2 seconds long epoch. In order to evaluate the performance of artificial neural network (ANN) in recognizing chronic and acute effects of heat stress with respect to the control subjects, unsupervised learning algorithm was applied on EEG data. Mean performance of SOFM with quadratic taper function was found to be better (chronic-92.6%, acute-93.2%) over the other two tapers. The effect of LVQ after the initial SOFM training seems explicit giving rise to considerable improvements in performance in terms of selectivity and sensitivity. The percentage increase in selectivity with uniform taper function is maximum for chronic and its control group (4.01%) and minimum for acute group (1.29%) whereas, with Gaussian it is almost identical (chronic-2.57%, acute-2.03%, control- 2.33%). Quadratic taper function gives rise to an increase of 2.41% for chronic, 1.96% for acute and 2.91% for control patterns.

Continuous wavelet transform (CWT) method has been applied to capture localized time-frequency information of rat electroencephalogram (EEG) in different vigilance states and analyze alterations in transients during awake, slow wave sleep (SWS), and rapid eye movement (REM) sleep stages due to exposure to high environmental heat. Rats were divided in three group (i) acute heat stress-subjected to a single exposure for four hours in the Biological Oxygen Demand (BOD) incubator at 38?C; (ii) chronic heat stress-exposed for 21 days daily for one hour in the incubator at 38?C, and (iii) handling control groups. After two hours long EEG recordings from young healthy rats, EEG data representing three sleep states was visually selected and further subdivided into 2 seconds long epoch. Powers of wavelet spectra corresponding to delta, theta, alpha, and beta bands at all scales and locations were computed and variation in their states investigated. The wavelet analysis of EEG signals following exposure to high environmental heat revealed that powers of subband frequencies vary with time unlike Fourier technique. Changes in higher frequency components (beta) were significant in all sleep-wake states following both acute and chronic heat stress conditions. Percentage power of different components of the four bands was always found to be varying at different intervals of time in the same signal of analysis.

Objective To demonstrate changes in different frequencies of cerebral electrical activity or electroencephalogram (EEG) following exposure to high environmental heat in three different age groups of freely moving rats. Methods Rats were divided into three groups (i) acute heat stress –subjected to a single exposure for four hours at 38 °C; (ii) chronic heat stress –exposed for 21 days daily for one hour at 38 °C, and (iii) handling control groups. The digital polygraphic sleep-EEG recordings were performed just after the heat exposure from acute stressed rats and on 22nd day from chronic stressed rats by simultaneous recording of cortical EEG, EOG (electrooculogram), and EMG (electromyogram). Further, power spectrum analyses were performed to analyze the effects of heat stress. Results The frequency analysis of EEG signals following exposure to high environmental heat revealed that in all three age groups of rats, changes in higher frequency components (β 2 ) were significant in all sleep-wake states following both acute and chronic heat stress conditions. After exposure to acute heat, significant changes in EEG frequencies with respect to their control groups were observed, which were reversed partly or fully in four hours of EEG recording. On the other hand, due to repetitive chronic exposure to hot environment, adaptive and long-term changes in EEG frequency patterns were observed. Conclusion The present study has exhibited that the cortical EEG is sensitive to environmental heat and alterations in EEG frequencies in different sleep-wake states due to heat stress can be differentiated efficiently by EEG power spectrum analysis.

Heat stress is known to induce high mortality rate due to multi-system illness, which demands urgent attention to reduce the fatality rate in such patients. Further, for the diagnosis and supportive therapy, one needs to define the severity of heat stress that can be distinguished as mild, intermediate and severe. The objective of this work is to develop an automated unsupervised artificial system to analyze the clinical outcomes of different levels of heat related illnesses. The Kohonen neural network program written in C++, which has seven normalized values of different clinical symptoms between 0-1 fed to the input layer of the network with 50 Kohonen output neurons, has been presented. The optimized initializing parameters such as neighborhood size and learning rate was set to 50 and 0.7, respectively, to simulate the network for 10 million iterations. The network was found smartly distinguishing all 51 patterns to three different states of heat illnesses. With the advent of these findings, it can be concluded that the Kohonen neural network can be used for automated classification of the severity of heat stress and other related psycho-patho-physiological disorders. However, to replace the expert clinicians with such type of smart diagnostic tool, extensive work is required to optimize the system with variety of known and hidden clinical and pathological parameters.

The effects of pre-treatment of para -chlorophenylalanine ( p -CPA) on sleep–wake electroencephalograms (EEG) have been demonstrated in three age groups of rats subjected to heat stress. Each age group for both p -CPA pre-treated and untreated subjects was sub-divided into three groups: (i) acute heat stress—subjected to a single heat exposure for 4 h at 38 °C; (ii) chronic heat stress—exposed for 21 days daily for 1 h in the incubator at 38 °C; and (iii) handling control groups. Digital polygraphic sleep recordings were performed just after the heat exposure from acute stressed rats and on the 22nd day from chronic stressed rats. The analyses of results demonstrated that many changes associated with sleep-EEG (either in sleep–wake parameter or in EEG frequencies) due to acute and chronic heat stress were reversed (changes were analyzed; P p -CPA pre-treated groups of rats. However, differential observations between acute and chronic heat stress groups of subjects were recorded, which are thought to have happened due to acclimatization of subjects to the hot environment. The results of present study supported the previous hypothesis about the significant involvement of serotonin in sleep–wake parameters and also demonstrated its participation in brain electrophysiological alterations in stressed conditions.

This Paper presents an automated method of Epileptic Spike detection in Electroencephalogram (EEG) using Deterministic Finite Automata (DFA). It takes pre-recorded single channel EEG data file as input and finds the occurrences of Epileptic Spikes data in it. The EEG signal was recorded at 256 Hz in two minutes separate data files using the Visual Lab-M software (ADLink Technology Inc., Taiwan). It was preprocessed for removal of baseline shift and band pass filtered using an infinite impulse response (IIR) Butterworth filter. A system, whose functionality was modeled with DFA, was designed. The system was tested with 10 EEG signal data files. The recognition rate of Epileptic Spike as on average was 95.68%. This system does not require any human intrusion. Also it does not need any short of training. The result shows that the application of DFA can be useful in detection of different characteristics present in EEG signals. This approach could be extended to a continuous data processing system.

The effects of chronic exposure (2 h daily for 21 days) of 1 kHz square wave-modulated 2450 MHz microwave radiation (non-thermal) on sleep-EEG, open field behavior, and thyroid hormones (T(3), T(4), and TSH) have been analyzed in an animal model. Results revealed significant changes in these pathophysiological parameters (p0.05 or better), except body temperature, grooming behavior, and TSH levels. The sleep-EEG power spectrum data for slow wave sleep (SWS), rapid eye movement (REM) sleep, and awake (AWA) states in two experimental groups of rats (microwave exposed and the control) were tested by an artificial neural network (ANN), containing 60 nodes in input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in hidden layer and an output node. The target output values for this network were determined with another five-layered neural network (with the structure of 6-14-1-14-6). The input and output of this network was assigned with the six confirmed pathophysiological changes. The most important feature for chronic exposure of 2450 MHz microwave exposure and for control subjects was extracted from the third layer single neuron and used as the target value for the three-layered ANN. The network was found effective in recognizing the EEG power spectra with an average of 71.93% for microwave exposure and 93.13% for control subjects, respectively. However, the lower percentage of pattern identification agreement in the microwave-exposed group in comparison to the control group suggest only mild effects of microwave exposure with this experimental setup.

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.

In an effort to develop new and more effective therapies to treat tuberculosis, a series of benzothiadiazine 1,1-dioxide derivatives were synthesized and their in vitro activity against Mycobacterium tuberculosis, Mycobacterium avium and Mycobacterium intracellulare was evaluated. One of the compounds, 8c, exhibited potent anti-tubercular activity, particularly for the resistant strains and thus prompted us to investigate its in vivo profile. However, the in vivo testing in a mouse model of tuberculosis infection did not show significant anti-tubercular activity, probably because of its poor bioavailability.

The effects of p-CPA (para-chlorophenylalanine) pretreatment was studied on the sleep-wake parameters and patterns of behavioral activities in an animal model of acute immobilization stress. For the experiments, young male Charles Foster rats were divided into three groups, subjected to (i) acute immobilization stress for four hours on specially designed wooden boards, (ii) a similar model of acute immobilization stress after pretreatment of p-CPA (injected through i.p. route), and (iii) control rats (p-CPA untreated and unstressed). Three channels of electrographic signals, i.e., EEG (electroencephalogram), EOG (electrooculogram), and EMG (electromyogram) were recorded continuously for four hours for all three groups of rats to analyze the changes in sleep-wake stages. The assessment of behavior was performed just after the stress on separate groups of rats in Open-Field (OF) and Elevated Plus-Maze (EPM) apparatuses. The significant changes in total sleep time (P < 0.05), total time for rapid eye movement sleep (P < 0.01), and total time in wakefulness (P < 0.01) following acute immobilization stress were found reversed in the p-CPA (a serotonin inhibitor) pretreated group of rats. Simultaneously, the results of the present work also revealed that the changes in grooming behavior (P < 0.05) in OF and the total time spent on the center of EPM (P < 0.05) were observed altered in p-CPA pretreated group of rats.

In this study, the Non Linear Autoregressive model of the resting electroencephalogram (EEG) was examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic (n = 20) conditions and the control group (n = 20). Data were taken from motor cortex region. The dimension of the extracted features are reduced by linear discrimination analysis (LDA) and classified by Support Vector Machine (SVM). The 600 sample from each group gave the best result using Support Vector Machine classifier. The maximum classification accuracy (90 %) with SVM clustering was achieved with the EEG Fz channel. More alterations are identified in the left hemisphere. Considering the good classification accuracy with SVM on Fz electrode, it can be suggested that the non-invasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

This work emphasizes to automatically detect the pattern called ‘epileptic spike’ from electroencephalogram (EEG) signal using multilayer perceptron (MLP). The analysis work is carried out through using the receiver operating characteristics (ROC). Electroencephalograph is used to record the electrical activity of the brain. Classification of the (EEG) signal plays a vital role in the diagnosis of epilepsy. The verification of epileptic seizure requires long-term EEG monitoring of 24 h or more. The signal is a huge collection of data and unfortunately, medicos uses the traditional method of visually interpreting EEG signal through personal experience to identify the transient event of epilepsy. This method of visual interpretation is tedious and time-consuming and, may result in an erroneous judgment. Hence, efficient EEG signal analysis is required for the diagnosis of epilepsy. This model is evaluated on the basis of sensitivity and selectivity and experimental result highlights the good precision of the model. The overall accuracy of the model is computed as 99.11 %.

Alterations in transients during awake, slow wave sleep (SWS), and rapid eye movement (REM) sleep stages due to an exposure to high environmental heat have been studied using continuous wavelet transform (CWT) method and artificial neural network (ANN). After two hours long EEG (Electroencephalogram) recordings from healthy rats, EEG data representing three sleep states was visually selected and further subdivided into 2 seconds long epoch. After extracting features in terms of wavelet coefficients for all the epochs multilayer perceptron neural network (MLPNN) has been trained to detect changes in the vigilance states of the subjects exposed to environmental heat stress. It reveals that, the classifications of wavelet coefficients of EEG signals in acute as well as chronic heat conditions along with the control data show the overall accuracy of 94.5% in SWS, 91.75% in REM sleep and 91.75% in AWAKE state.

The odd mass nucleus 137Pm has been studied to high spins through the 109Ag(32S, 2p2n)137Pm reaction at an incident beam energy of 150 MeV. The de-exciting γ-rays were detected using an array of 18 Compton suppressed clover detectors. The level scheme of 137Pm has been extended up to $$J^\pi = \tfrac{{43}} {2}^ -$$ and excitation energy of E x ≅ 6 MeV with the observation of 42 new gamma transitions. The linear polarization (IPDCO) measurements for the γ-ray transitions have been done for the first time. The spin and parity assignments for most of the reported levels have been made using these results and the coincidence angular anisotropy (RDCO) measurements. The nuclear shape evolution is discussed in the light of Total Routhian Surface (TRS) and Cranked Shell Model (CSM) calculations.

The present work aims to simulate and predict the molecular pathway of Parkinson’s disease (PD) initiated by environmental stress. It is designed to predict how protein aggregation and Lewy body (LB) formation leads to dopamine toxicity finally leading to cell death. The Mechanistic electronic modeling using Digital-Analog hybrid technique has been designed and implemented using MULTISIM 8.0 (National Instruments, USA) to simulate and predict the dynamics of PD pathway under environmental stress condition. The electronic network is initiated through an analog comparator circuit that compares the long-term environmental alterations with the cellular set point level of the molecular kinetics. The pattern of neuronal firing is simulated with the help of analog electronic circuit. The simulation results show a slow progressive rise in [Formula: see text]-synuclein/LB/dopamine toxicity level within the neuronal cells, which is comparable to published experimental and modeling studies associated with PD. Finally, cell death is demonstrated, which leads to neurodegeneration. In conclusion, our model allows a straightforward implementation of qualitative representation for systems where the network topology or pathway is at least moderately known.

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.

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)

Backpropagation artificial neural network (ANN) has been designed to classify sleep---wake stages. Four hours continuous three channel polygraphic signals such as EEG (electroencephalogram), EOG (electrooculogram) and EMG (electromyogram) from conscious subjects were digitally recorded and stored in computer. EOG and EMG signals were used for manual identification of sleep states before training and testing of ANN. The percentages power of the 2 s epochs of the digitized EEG signals from each of three sleep---wake patterns, sleep spindles (SS), rapid eye movement (REM) sleep and awake (AWA) sates, were calculated and analyzed to select the manually confirmed sleep---wake states for each epoch. Further, second order Daubechies mother wavelet has been used to get the wavelet coefficients for the selected EEG epochs. The wavelet coefficients for the EEG epochs (64 data) were selected as inputs for the training the network and to classify SS, REM sleep and AWA stages. The ANN architecture used (64---14---3) in present study shows overall very good agreement with manual sleep stage scoring with an average of 95.35% for all the 1,140 samples tested from SS, REM and AWA stages. This architecture of ANN was also found effectively differentiating the EEG power spectra from different sleep---wake states (96.84% in SS, 93.68% in REM sleep, 95.52% in AWA state). The high performance observed with the system based on wavelet coefficients along with the ANN, highlights the need of this computational tool into the field of sleep research.