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Objective: Hepatic Steatosis is a disorder with high prevalence among obese people. Traditional imaging modalities are more common in hepatic steatosis diagnosis, but they are not suitable for monitoring or treatment evaluation. This study aims at developing a new technique suitable for electromagnetic (EM) tool in the microwave band to differentiate steatotic from non-steatotic liver. Methods: A differential permittivity estimation method for hepatic steatosis detection is proposed. First, the effective permittivity of the right side of the torso is estimated based on the phase difference of electromagnetic waves traveling along symmetric paths within the torso. Then, permittivity modeling and statistical frequency selection are performed to model the estimated values and to extract reliable frequency samples. Finally, the percentage of the difference between the permittivity of the left and right sides of the torso is calculated over the selected samples. Results: The effectiveness of the proposed method is validated using simulated signals and phantom measurements. The analyzed results reveal higher contrast between the average permittivity of the left and right sides of the torso for cases with hepatic steatosis (average contrast of 29.2.

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 0.096×0.048×0.048λ3. 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.

ObjectiveIn publications on the electroencephalographic (EEG) features of psychoses and other disorders, various methods are utilised to diminish electromyogram (EMG) contamination. The extent of residual EMG contamination using these methods has not been recognised. Here, we seek to emphasise the extent of residual EMG contamination of EEG.MethodsWe compared scalp electrical recordings after applying different EMG-pruning methods with recordings of EMG-free data from 6 fully-paralysed healthy subjects. We calculated the ratio of the power of pruned, normal scalp electrical recordings in the 6 subjects, to the power of unpruned recordings in the same subjects when paralysed. We produced “contamination graphs” for different pruning methods.ResultsEMG contamination exceeds EEG signals progressively more as frequencies exceed 25 Hz and with distance from the vertex. In contrast, Laplacian signals are spared in central scalp areas, even to 100 Hz.ConclusionGiven probable EMG contamination of EEG in psychiatric and other studies, few findings on beta- or gamma-frequency power can be relied upon. Based on the effectiveness of current methods of EEG de-contamination, investigators should be able to re-analyse recorded data, re-evaluate conclusions from high frequency EEG data and be aware of limitations of the methods.

Objective To compare comprehensive measures of scalp-recorded muscle activity in migraineurs and controls. Method We used whole-of-head high-density scalp electrical recordings, independent component analysis (ICA) and spectral slope of the derived components, to define muscle (electromyogram-containing) components. After projecting muscle components back to scalp, we quantified scalp spectral power in the frequency range, 52–98 Hz, reflecting muscle activation. We compared healthy subjects (n = 65) and migraineurs during a non-headache period (n = 26). We also examined effects due to migraine severity, gender, scalp-region and task (eyes-closed and eyes-open). We could not examine the effect of pre-ictal versus inter-ictal versus post-ictal as this information was not available in the pre-existing dataset. Results There was more power due to muscle activity (mean ± SEM) in migraineurs than controls (respectively, −13.61 ± 0.44 dB versus −14.73 ± 0.24 dB, p = 0.028). Linear regression showed no relationship between headache frequency and muscle activity in any combination of region and task. There was more power during eyes-open than eyes-closed (respectively, −13.42 ± 0.34 dB versus −14.92 ± 0.34 dB, p = 0.002). Conclusions There is an increase in cranial and upper cervical muscle activity in non-ictal migraineurs versus controls. This raises questions of the role of muscle in migraine, and the possible differentiation of non-ictal phases. Significance This provides preliminary evidence to date of possible cranial muscle involvement in migraine.

Background Contamination of scalp measurement by tonic muscle artefacts, even in resting positions, is an unavoidable issue in EEG recording. These artefacts add significant energy to the recorded signals, particularly at high frequencies. To enable reliable interpretation of subcortical brain activity, it is necessary to detect and discard this contamination. New method We introduce a new automatic muscle-removal approach based on the traditional Blind Source Separation-Canonical Correlation Analysis (BSS-CCA) method and the spectral slope of its components. We show that CCA-based muscle-removal methods can discriminate between signals with high correlation coefficients (brain, mains artefact) and signals with low correlation coefficients (white noise, muscle). We also show that typical BSS-CCA components are not purely from one source, but are mixtures from multiple sources, limiting the performance of BSS-CCA in artefact removal. We demonstrate, using our paralysis dataset, improved performance using BSS-CCA followed by spectral-slope rejection. Result This muscle removal approach can reduce high-frequency muscle contamination of EEG, especially at peripheral channels, while preserving steady-state brain responses in cognitive tasks. Comparison with existing methods This approach is automatic and can be applied on any sample of data easily. The results show its performance is comparable with the ICA method in removing muscle contamination and has significantly lower computational complexity. Conclusion We identify limitations of the traditional BSS-CCA approach to artefact removal in EEG, propose and test an extension based on spectral slope that makes it automatic and improves its performance, and results in performance comparable to competitors such as ICA-based artefact removal.

Objective To present a new, automated and fast artefact-removal approach which significantly reduces the effect of contamination in scalp electrical recordings. Method We used spectral and temporal characteristics of different sources recorded during a typical scalp electrical recording in order to improve a fast and effective artefact removal approach. Our experiments show that correlation coefficient and spectral gradient of brain components differ from artefactual components. We trained two binary support vector machine classifiers such that one separates brain components from muscle components, and the other separates brain components from mains power and environmental components. We compared the performance of the proposed approach with seven currently used alternatives on three datasets, measuring mains power artefact reduction, muscle artefact reduction and retention of brain neurophysiological responses. Results The proposed approach significantly reduces the main power and muscle contamination from scalp electrical recording without affecting brain neurophysiological responses. None of the competitors outperformed the new approach. Conclusions The proposed approach is the best choice for artefact reduction of scalp electrical recordings. Further improvements are possible with improved component analysis algorithms. Significance This paper provides a definitive answer to an important question: Which artefact removal algorithm should be used on scalp electrical recordings?

In this paper, we perform the first comparison of a large variety of effective connectivity measures in detecting causal effects among observed interacting systems based on their statistical significance. Well-known measures estimating direction and strength of interdependence between time series are compared: information theoretic measures, model-based multivariate measures in the time and frequency domains, and phase-based measures. The performance of measures is tested on simulated data from three systems: three coupled Henon maps; a multivariate autoregressive (MVAR) model with and without EEG as an exogenous input; and simulated EEG. No measure was consistently superior. Measures that model the data as MVAR perform well when the data are drawn from that model. Frequency domain measures perform well when the data have a clearly defined band of interest. When neither of these is true, information theoretic measures perform well. Overall, the measure with the best performance in a variety of situations and with a low computational cost is conditional Granger causality. Partial Granger causality and multivariate Granger causality are also good measures, but their computational cost rises rapidly with the number of channels. Copula Granger causality can also be used reliably, but its computational cost rises rapidly with the number of data.

We compare the performance of many effective connectivity measures in detecting statistically significant causal connections between time series drawn from linear and nonlinear coupled systems. Fifteen measures are compared, drawn from two families (information theoretic, and frequency- and time-based multivariate autoregressive models), including common and uncommon measures. Measures were tested on simulated data from three systems: three coupled Henon maps; a multivariate autoregressive (MVAR) model with and without EEG as an exogenous input; and simulated EEG. Comparisons focus on the effective of parameter choices, e.g. maximum model order or maximum number of lags, for different lengths of data. Performance varies with dataset, and no measure was outstanding for all datasets. Strong performance is obtained where the measure’s model and data source match (eg MVAR model, or frequency domain measures with narrowband data). When there is no match, information theoretic measures and Copula Granger causality generally perform best.

Scalp electrical recordings, or electroencephalograms (EEG), are heavily contaminated by cranial and cervical muscle activity from as low as 20 hertz, even in relaxed conditions. It is therefore necessary to reduce or remove this contamination to enable reliable exploration of brain neurophysiological responses. Scalp measurements record activity from many sources, including neural and muscular. Independent Component Analysis (ICA) produces components ideally corresponding to separate sources, but the number of components is limited by the number of EEG channels. In practice, at most 30% of components are cleanly separate sources. Increasing the number of channels results in more separate components, but with a significant increase in costs of data collection and computation. Here we present results to assist in selecting an appropriate number of channels. Our unique database of pharmacologically paralysed subjects provides a way to objectively compare different approaches to achieving an ideal, muscle free EEG recording. We evaluated an automatic muscle-removing approach, based on ICA, with different numbers of EEG channels: 21, 32, 64, and 115. Our results show that, for a fixed length of data, 21 channels is insufficient to reduce tonic muscle artefact, and that increasing the number of channels to 115 does result in better tonic muscle artefact reduction.

Scalp electroencephalograms (EEG) are susceptible to cranial and cervical muscle contamination from frequencies as low as 20 hertz, even in relaxed conditions. Reliably recording cognitive activity, which is in this range, is impossible without removing or reducing the effect of muscle contamination. Our unique database of paralysed conscious subjects enabled us to test the effect of combining beamforming and blind source separation in reducing tonic muscle contamination of scalp electrical recordings. Using the beamforming technique, muscle sources are separated automatically based on their location; while using blind source separation, muscle components are separated based on their spectral gradient. Our results show that applying the beamforming technique on data pruned by a blind source separation technique (or vice versa) can reduce tonic muscle contamination significantly more than applying either of them separately, especially at peripheral locations. Hence, these approaches complement each other in reducing muscle contamination of EEG.

© 2018 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 12 month embargo from date of publication (December 2018) in accordance with the publisher’s archiving policy, 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.

Background Cranial and cervical muscle activity (electromyogram, EMG) contaminates the surface electroencephalogram (EEG) from frequencies below 20 through to frequencies above 100 Hz. It is not possible to have a reliable measure of cognitive tasks expressed in EEG at gamma-band frequencies until the muscle contamination is removed. New method In the present work, we introduce a new approach of using a minimum-norm based beamforming technique (sLORETA) to reduce tonic muscle contamination at sensor level. Using a generic volume conduction model of the head, which includes three layers (brain, skull, and scalp), and sLORETA, we estimated time-series of sources distributed within the brain and scalp. The sources within the scalp were considered to be muscle and discarded in forward modelling. Result (1) The method reduced EMG contamination, more strongly at peripheral channels; (2) task-induced cortical activity was retained or revealed after removing putative muscle activity. Comparison with existing methods This approach can decrease tonic muscle contamination in scalp measurements without relying on time-consuming processing of expensive MRI data. In addition, it is competitive to ICA in muscle reduction and can be reliably applied on any length of recorded data that captures the dynamics of the signals of interest. Conclusion This study suggests that sLORETA can be used as a method to quantitate cranial muscle activity and reduce its contamination at sensor level.

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.

Parkinson's disease (PD) is a widespread progressive neural degenerative disease. Patients encounter problems in carrying out ordinary voluntary movements such as gaiting and writing. Effects of tremor, rigidity, and bradykinesia are seen in writing. Since the diagnosis of PD is based on the presence of cardinal symptoms, therefore voluntary movements like writing could be helpful as an indicator in this process. Handwriting analysis can be a suitable method to separate patients from normal individuals. We recorded handwriting data from 17 normal and 13 PD subjects, and then used mathematical analysis in order to extract proper features. At the end of the study, we used these selected features in a classifier. We achieved 93.89% accuracy in the test phase. Hence, this tool may be aid in the diagnosing of both PD and suspected PD individuals in the early stages of the disease.