Coupling Functions between Brain Waves: Significance of Opened/Closed Eyes

In dynamical systems, the information flows converge or diverges in state space and is integrated or communicated between different cells assemblies termed as CFC. This process allows different oscillatory systems to communicate in accurate time, control and distribute the information flows in cell assemblies. The CF interactions allow the oscillatory rhythms to communicate in accurate time, and reintegrate the separated information. The intrinsic brain dynamics in Electroencephalography (EEG) with eye - closed (EC) and eye open (EO) during resting states have been investigated to see the changes in brain complexity i.e. simple visual processing which are associated with increase in global dimension complexity. In order to study these changes in EEG, we have computed the coupling to see the inhibitory interneurons response and inter-regions functional connectivity differences between the eye conditions. We have investigated the fluctuations in EEG activities in low (delta, theta) and high (alpha) frequency brain oscillations. Coupling strength was estimated using Dynamic Bayesian inference approach which can effectively detect the phase connectivity subject to the noise within a network of time varying coupled phase oscillators. Using this approach, we have seen that delta-alpha and theta-alpha CFC are more dominant in resting state EEG and applicable to multivariate network oscillator. It shows that alpha phase was dominated by low frequency oscillations i.e. delta and theta. These different CFC help us to investigate complex neuronal brain dynamics at large scale networks. We observed the local interactions at high frequencies and global interactions at low frequencies. The alpha oscillations are generated from both posterior and anterior origins whereas the delta oscillations found at posterior regions

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques

The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients

Enhancement and Assessment of a Code-Analysis-Based Energy Estimation Framework

Energy estimation of applications helps developers greening the smartphone- and Internet-of-Things-based devices. Traditional energy estimation schemes consider smartphone component's power measurement or code analysis methods for energy estimation of applications. The existing code analysis method considers the energy cost of software operations to minimize the energy estimation overhead of dynamic estimation methods. However, it overlooked cache storage analysis and overheads associated with it due to concurrent program execution at runtime. As a result, the performance of estimation tools is affected. To handle these issues, this study put forward an enhanced static-code-analysis-based lightweight energy estimation (SA-LEE) framework that has considered overheads associated with the application runtime execution environment, cache storage analysis, and the application inactivity period for energy estimation of applications. The experiments revealed that the SA-LEE model has minimized the estimation time and the energy overhead by 98% and 97%, respectively. Also, the accuracy is observed to be 82-88%. © 2018 IEE

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Mean ranks for comparison of a) MNCSE and b) MSE at temporal scales 1 to 15 for quantifying the dynamics of pathological with disease severity.

<p>Mean ranks for comparison of a) MNCSE and b) MSE at temporal scales 1 to 15 for quantifying the dynamics of pathological with disease severity.</p

MSE (a-f) and MNCSE (g-l) as a function of data length, computed from 40 realizations of WGN and 1/f noise signals.

<p>MSE (a-f) and MNCSE (g-l) as a function of data length, computed from 40 realizations of WGN and 1/f noise signals.</p

Classification accuracy (CA), computed using (a)10 by 10 FCV for separating NSR and CHF subjects (b) 10 by 10 FCV for separating NSR young and NSR elderly subjects (c) leave-one-out cross validation for separating NSR and CHF subjects (d) leave-one-out cross validation for separating NSR young and NSR elderly subjects.

<p>Classification accuracy (CA), computed using (a)10 by 10 FCV for separating NSR and CHF subjects (b) 10 by 10 FCV for separating NSR young and NSR elderly subjects (c) leave-one-out cross validation for separating NSR and CHF subjects (d) leave-one-out cross validation for separating NSR young and NSR elderly subjects.</p

Illustration of coarse-graining procedure using mean of data points in a non-overlapping window equal in length to the time scale.

<p>Illustration of coarse-graining procedure using mean of data points in a non-overlapping window equal in length to the time scale.</p