Extents of Predictors for Land Surface Temperature Using Multiple Regression Model.

Land surface temperature (LST) is a key factor in numerous areas such as climate change, land use/land cover in the urban areas, and heat balance and is also a significant participant in the creation of climate models. Landsat data has given numerous possibilities to understand the land processes by means of remote sensing. The present study has been performed to identify the LST of the study region using Landsat 8 OLI/TIRS satellite images for two time periods in order to compare the data. The study also attempted to identify and predict the role and importance of NDVI, NDBI, and the slope of the region on LST. The study concludes that the maximum and minimum temperatures of 40.44 C and 20.78 C were recorded during the November month whereas the maximum and minimum LST for month March has increased to 42.44 C and 24.57 C respectively. The result indicates that LST is inversely proportional to NDVI (-6.369) and slope (-0.077) whereas LST is directly proportional to NDBI (+14.74). Multiple linear regression model has been applied to calculate the extents of NDVI, NDBI, and slope on the LST. It concludes that the increase in vegetation and slope would result in slight decrease in temperature whereas the increase in built-up will result in a huge increase in temperature.

The Effect of Lateralization of Motor Onset and Emotional Recognition in PD Patients Using EEG.

The objective of this research was to investigate the relationship between emotion recognition and lateralization of motor onset in Parkinson's disease (PD) patients using electroencephalogram (EEG) signals. The subject pool consisted of twenty PD patients [ten with predominantly left-sided (LPD) and ten with predominantly right-sided (RPD) motor symptoms] and 20 healthy controls (HC) that were matched for age and gender. Multimodal stimuli were used to evoke simple emotions, such as happiness, sadness, fear, anger, surprise, and disgust. Artifact-free emotion EEG signals were processed using the auto regressive spectral method and then subjected to repeated ANOVA measures. No group differences were observed across behavioral measures; however, a significant reduction in EEG spectral power was observed at alpha, beta and gamma frequency oscillations in LPD, compared to RPD and HC participants, suggesting that LPD patients (inferred right-hemisphere pathology) are impaired compared to RPD patients in emotional processing. We also found that PD-related emotional processing deficits may be selective to the perception of negative emotions. Previous findings have suggested a hemispheric effect on emotion processing that could be related to emotional response impairment in a subgroup of PD patients. This study may help in clinical practice to uncover potential neurophysiologic abnormalities of emotional changes with respect to PD patient's motor onset.

Inter-hemispheric EEG coherence analysis in Parkinson's disease: assessing brain activity during emotion processing.

Parkinson's disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3-AF4, F7-F8, F3-F4, FC5-FC6, T7-T8, P7-P8, and O1-O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities.

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study.

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.

Emotion processing in Parkinson's disease: an EEG spectral power study.

OBJECTIVE: Although an emotional deficit is a common finding in Parkinson's disease (PD), its neurobiological mechanism on emotion recognition is still unknown. This study examined the emotion processing deficits in PD patients using electroencephalogram (EEG) signals in response to multimodal stimuli. METHOD: EEG signals were investigated on both positive and negative emotions in 14 PD patients and 14 aged-matched normal controls (NCs). The relative power (i.e., ratio of EEG signal power in each frequency band compared to the total EEG power) was computed over three brain regions: the anterior (AF3, F7, F3, F4, F8 and AF4), central (FC5 and FC6) and posterior (T7, P7, O1, O2, P8 and T8) regions for theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (30-60 Hz) frequency sub-bands, respectively. RESULTS: Behaviorally, PD patients showed decreased performance in classifying emotional stimuli as measured by subjective ratings. EEG power at theta, alpha, beta, and gamma bands in all regions were significantly different between the NC and PD groups during both the emotional tasks, with p-values less than 0.05. Furthermore, an increase of relative spectral powers in the theta and gamma bands and a decrease of relative powers in the alpha and beta bands were observed for PD patients compared with NCs during emotional information processing. CONCLUSION: The results suggest the possibility of the existence of a distinctive neurobiological substrate of PD patients during emotional information processing. Also, these distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients.

Regulation of pro and anti-inflammatory signaling molecules in effect of Withania Somnifera root extract treated sleep deprivation induced Wistar rats.

Sleep plays an imperative role in maintaining good health. Sleep along with circadian cycle wields strong regulatory control over immunity. Sleep deprivation (SD) is a threat to health developing several immunological disorders. The medicinal plant Withania somnifera (WS) root extract is widely used for its immuno-modulatory properties. Therefore, it is of interest to assess the effect of WS root extract on pro and anti-inflammatory signalling in SD rats. 24 male Wistar rats (120-150g) were divided into 4 groups with 6 animals in each. The groups were divided such that Group I - cage control, Group II - large platform control, Group III - sleep deprived & Group IV - WS treated SD rats. RT-PCR based mRNA expression analysis of pro inflammatory (IL-1ß, IL-6, MCP-1, TNF-α) and anti-inflammatory marker (IL-10) in the cortex of control and SD rats were completed. Concurrent protein expression analysis was completed using western blot. Data was analyzed using one-way ANOVA and Duncan's multiple range test in SPSS software version 20. Data showed elevation of pro-inflammatory markers and depression of IL-10. Thus, WS down regulated the pro-inflammatory and up-regulated the anti-inflammatory molecules, which can be further considered towards the treatment of sleep deprivation induced inflammatory diseases.

Effect of Withania Somnifera on the antioxidant and neurotransmitter status in sleep deprivation induced Wistar rats.

Sleep is normally a period of relaxation and repair, important for the maintenance of physiological homeostasis and psychological balance. "Globally, millions of people experiences sleep deprivation daily". Sleep deprivation (SD) impairs cognitive functions, decreases anti-oxidative defense and induces neuronal changes. Withania somnifera (WS), commonly known as an "Indian Ginseng" has broad therapeutic applications, including anti-inflammatory activities, actions on immune system, circulatory system, central nervous system etc., The study is aimed to assess effect of Withania somnifera on antioxidant status and neurotransmitter level in sleep deprivation induced male Wistar albino rats. The study was done in the Department of Physiology, Meenakshi Medical College and Hospital, Enathur, Kanchipuram. 24 male adult Wistar rats weighing 120-150g were used for the study. They were divided into 4 groups with 6 animals in each group. (Group I - cage control, Group II - large platform control, Group III - sleep deprived group and Group IV - WS treated SD group). Animals were deprived sleep for one week using a modified multiple platform method. Oxidative stress parameters and antioxidant enzymes were measured using spectrophotometry. Neurotransmitters such as dopamine and serotonin concentration in the serum were measured by ELISA method. There was a marked (by one-way ANOVA test) decrease observed in the antioxidants enzymes in the cortex of both large platform control and sleep deprivation induced group. The group treated with W. somnifera root extract significantly reduced the free radical production and lipid peroxidation with simultaneous increase in the level of antioxidant enzymes compared to the untreated group. Also in our study the concentration of dopamine and serotonin was found to be significantly reduced (p < 0.05) in sleep deprived (SD) and large platform control group when compared to cage control group. Whereas the group treated with W. somnifera (400mg/kg b.wt) increased the neurotransmitter levels significantly. Withania somnifera proved to be an effective therapeutic agent by maintaining the antioxidant status and neurotransmitter levels.

Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands.

Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challenging problem. Deep learning methods can be an automated way to perform this task. In this work, we present a new approach based on convolutional neural network (CNN) to detect IEDs from EEGs automatically. The input to CNN is a combination of raw EEG and frequency sub-bands, namely delta, theta, alpha and, beta arranged as a vector for one-dimensional (1D) CNN or matrix for two-dimensional (2D) CNN. The proposed method is evaluated on 554 scalp EEGs. The database consists of 18,164 IEDs marked by two neurologists. Five-fold cross-validation was performed to assess the IED detectors. The resulting 1D CNN based IED detector with multiple sub-bands achieved a false positive rate per minute of 0.23 and a precision of 0.79 at 90% sensitivity. Further, the proposed system is evaluated on datasets from three other clinics, and the features extracted from CNN outputs could significantly discriminate (p-values <; 0.05) the EEGs with and without IEDs. We have proposed an optimized method with better performance than the literature that could aid clinicians to diagnose epilepsy expeditiously, and thereby devise proper treatment.

Detection of Subthalamic Nucleus using Time-Frequency Features of Microelectrode recordings and Random Forest Classifier.

Accurate localization of subthalamic nucleus (STN) is a key prior in deep brain stimulation (DBS) surgery for the patients with advanced Parkinson's disease (PD). Microelectrode recordings (MERs) along with preplanned trajectories are often employed for the STN localization and it remains challenging task. These MER signals are nonstationary and multicomponent in nature. In this study, we propose a system based on time-frequency features of MERs to differentiate the STN and non-STN regions. We assessed the system with 50 MER trajectories from 26 PD patients who have undergone DBS surgery. The signals are pre-processed and subjected to six-level wavelet decomposition. Then, the entropy is computed from the detailed and approximate coefficients. These features are fed to the random forest classifier and the model is evaluated by leave one patient out cross-validation. The results show that entropy associated with detailed wavelet coefficients (D1and D2) are higher in STN where as it is lower in other wavelet scales. All extracted features except entropy from approximate coefficients are found to have significant difference between non-STN and STN (p<; 0.05). The random forest classifier achieves about 83% accuracy and 87% precision in differentiating the STN and non-STN regions.

Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson's disease patients.

Recent studies show right hemisphere has a unique contribution to emotion processing. The present study investigated EEG using non-linear measures during emotional processing in PD patients with respect to motor symptom asymmetry (i.e., most affected body side). We recorded 14-channel wireless EEGs from 20 PD patients and 10 healthy age-matched controls (HC) by eliciting emotions such as happiness, sadness, fear, anger, surprise and disgust. PD patients were divided into two groups, based on most affected body side and unilateral motor symptom severity: left side-affected (LPD, n = 10) or right side-affected PD patients (RPD, n = 10). Nonlinear analysis of these emotional EEGs were performed by using approximate entropy, correlation dimension, detrended fluctuation analysis, fractal dimension, higher order spectra, hurst exponent (HE), largest Lyapunov exponent and sample entropy. The extracted features were ranked using analysis of variance based on F value. The ranked features were then fed into classifiers namely fuzzy K-nearest neighbor and support vector machine to obtain optimal performance using minimum number of features. From the experimental results, we found that (a) classification performance across all frequency bands performed well in recognizing emotional states of LPD, RPD, and HC; (b) the emotion-specific features were mainly related to higher frequency bands; and (c) predominantly LPD patients (inferred right-hemisphere pathology) were more impaired in emotion processing compared to RPD, as showed by a poorer classification performance. The results suggest that asymmetric neuronal degeneration in PD patients may contribute to the impairment of emotional communication.

Brain functional connectivity patterns for emotional state classification in Parkinson's disease patients without dementia.

Successful emotional communication is crucial for social interactions and social relationships. Parkinson's Disease (PD) patients have shown deficits in emotional recognition abilities although the research findings are inconclusive. This paper presents an investigation of six emotions (happiness, sadness, fear, anger, surprise, and disgust) of twenty non-demented (Mini-Mental State Examination score >24) PD patients and twenty Healthy Controls (HCs) using Electroencephalogram (EEG)-based Brain Functional Connectivity (BFC) patterns. The functional connectivity index feature in EEG signals is computed using three different methods: Correlation (COR), Coherence (COH), and Phase Synchronization Index (PSI). Further, a new functional connectivity index feature is proposed using bispectral analysis. The experimental results indicate that the BFC change is significantly different among emotional states of PD patients compared with HC. Also, the emotional connectivity pattern classified using Support Vector Machine (SVM) classifier yielded the highest accuracy for the new bispectral functional connectivity index. The PD patients showed emotional impairments as demonstrated by a poor classification performance. This finding suggests that decrease in the functional connectivity indices during emotional stimulation in PD, indicating functional disconnections between cortical areas.

Preliminary studies of left ventricular wall thickness and mass of normotensive and hypertensive subjects using m-mode echocardiography.

The relationship between left ventricular mass (LVM) and the mean arterial blood pressure (MAP) was investigated, using M-Mode echocardiography. MAP was higher in hypertensive patients (p<0.05, n=9) compared to that of controlled subjects. The results showed that LVM index for hypertensive patients was significantly higher (p<0.05, n=9) than that for the normal group. LVM index correlates fairly (r=0.6) with MAP for hypertensive patients. The results also show that the increase of intraventricular septal wall thickness (IVST) was due to hypertension. The LVM (r =0.9) and IVST (r=0.75) of the normal subjects were linearly dependent on the body surface area (BSA). The hypertensive group revealed a non-linear relationship to the BSA.

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease.

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.