In neuroscience, there is considerable current interest in investigating the connections between different parts of the brain. EEG is one modality for examining brain function, with advantages such as high temporal resolution and low cost. Many measures of connectivity have been proposed, but which is the best measure to use? In this paper, we address part of this question: which measure is best able to detect connections that do exist, in the challenging situation of non-stationary and noisy data from nonlinear systems, like EEG. This requires knowledge of the true relationship between signals, hence we compare 26 measures of functional connectivity on simulated data (unidirectionally coupled Hénon maps, and simulated EEG). To determine whether synchrony is detected, surrogate data were generated and analysed, and a threshold determined from the surrogate ensemble. No measure performed best in all tested situations. The correlation and coherence measures performed best on stationary data with many samples. S-estimator, correntropy, mean-phase coherence (Hilbert), mutual information (kernel), nonlinear interdependence (S) and nonlinear interdependence (N) performed most reliably on non-stationary data with small to medium window sizes. Of these, correlation and S-estimator have execution times that scale slower with the number of channels and the number of samples. Keywords-Connectivity, EEG, biomedical signal processing, nonstationarity. Highlights:• Extensive comparative study of 26 functional connectivity measures for EEG • Measures compared on simulated noisy and nonstationary data • Surrogates used to determine threshold for significant connectivity • 8 measures performed well, choice of best depends on the particular situation • Correlation coefficient and S-estimator measures performed best overall M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 2 synchronisation means adjustment of frequencies of periodic self-sustained oscillators due to weak interaction" [2]. The concept of synchronisation has been generalised to the case of chaotic oscillatory systems with irregular behaviour. The study of synchronisation between signals from such systems has been a topic of increasing interest, and has found applications in areas such as laser dynamics, solid state physics, electronics, biology, medicine, communication and even economics. There has been wide-ranging research aimed at detecting underlying relationships (which may be nonlinear and/or nonstationary) in multi-output dynamic systems, to give useful insight into their spatio-temporal organisation [3]. Synchronisation can manifest itself in different ways, hence a large variety of measures have been proposed to quantify synchronisation between signals. Synchronisation can occur due to one source driving another, and in such situations there is a direction to the relationship. Functional connectivity measures are symmetric and so cannot detect a direction in a relationship.Effective connectivity measures are not symmetric, and do detect a direction. T...

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This paper presents a robust device for automated screening of pediatric heart diseases based on our unique processing method in murmur characterization; the Arash-Band method. The present study modifies the Arash-Band method and employs output of the modified method in conjunction with the two other original techniques to extract indicative feature vectors for the screening. The extracted feature vectors are classified by using the support vector machine method. Results show that the proposed modifications significantly enhances performance of the Arash-Band in terms of the both accuracy and sensitivity as the corresponding effect sizes are sufficiently large. The proposed algorithm has been incorporated into an Android-based tablet to constitute an intelligent phonocardiogram with the automatic screening capability. In order to obtain confidence interval of the accuracy and sensitivity, an inferable statistical test is applied on our database containing the phonocardiogram signals recorded from 263 of the referrals to a hospital. The expected value of the accuracy/sensitivity is estimated to be 87.45 % / 87.29 % with a 95 % confidence interval of (80.19 % - 92.47 %) / (76.01 % - 95.78 %) exhibiting superior performance than a pediatric cardiologist who relies on conventional or even computer-assisted auscultation.

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A wearable electromagnetic belt system for the detection of hepatic steatosis (lipid accumulation within the major liver cells, hepatocytes), is proposed. To satisfy the requirements of the belt system, an array of body matched antennas is designed. The belt, which goes around the lower chest and over the liver, requires compact, wideband, unidirectional antennas that operate at low microwave frequencies. To avoid using conventional bulky reflector structures, the designed antenna utilizes the loop-dipole combination concept. To enhance electromagnetic wave penetration, the antenna is designed to match the human body. Thus, thanks to the high dielectric loading from the human body, the dipole element of the antenna is easily miniaturized. Since the same principle does not apply on the loop structure, meandered arc-shapes are employed to increase the effective electrical length of the loop. The final antenna design has a measured wide operating bandwidth of 0.58-1.6 GHz with a compact size of 0.096×0.048×0.048λ 3 . The proposed structure is effective in irradiating the torso, where the signal can reach center of the liver at a depth of 90 mm, with 64% of the peak radiated power. An electromagnetic belt is built using twelve elements of the designed antennas. The belt is then tested on a 3D printed torso phantom that includes models of the lungs and liver. Due to close dielectric properties of the other tissues inside the torso, these are represented using an average tissue mimicking mixture with permittivity of 46. Measured data are analyzed using multivariate energy statistics method. A peak measured dissimilarity of 15.1% between steatotic and healthy liver is attained. These initial tests and obtained results indicate the potential of the proposed system as a method to diagnose hepatic steatosis.INDEX TERMS Body-matched antenna, electromagnetic belt, fatty liver disease, statistical based analysis, wearable system.