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Recently, a circular symmetrical nonlinear stationary 2D differential model for biomedical micropumps, where the amplitude of the electrostatic field is locally proportional to the curvature of the membrane, was studied in detail. Starting from this, in this work, we first introduce a positive and limited function to model the dielectric properties of the material constituting the membrane according to experimental evidence which highlights that electrostatic capacitance variation occurs when the membrane deforms. Therefore, we present and discuss algebraic conditions of existence, uniqueness, and stability, even with the fringing field formulated according to the Pelesko–Driskoll theory, which is known to take these effects into account with terms characterized by reduced computational loads. These conditions, using "gold standard" numerical approaches, allow the optimal numerical recovery of the membrane profile to be achieved under different load conditions and also provide an important criterion for choosing the intended use of the device starting from the choice of the material constituting the membrane and vice versa. Finally, important insights are discussed regarding the pull-in voltage and electrostatic pressure. [ABSTRACT FROM AUTHOR]

The International Journal of Neural Systems has published a special issue featuring six papers selected from the 2nd International Conference on Artificial Intelligence & Informatics (AII2022). The conference focused on the reproducibility of research results and received 108 submissions from 20 countries, with 39 full papers accepted for presentation and publication. The selected papers in the special issue cover topics such as the application of artificial intelligence in biomedical engineering and clinical diagnosis, measuring the reputation of agents, and analyzing synthetic voices for cultural differences recognition. The authors of these papers come from Austria, Ireland, Italy, Spain, and the UK. [Extracted from the article]

In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-frequency-domain, as part of a hybrid strategy, to discriminate the temporal windows (i.e. EEG epochs) preceding hand sub-movements (open/close) and the resting state. To this end, for each EEG epoch, the associated cortical source signals in the motor cortex and the corresponding time-frequency (TF) maps are estimated via beamforming and Continuous Wavelet Transform (CWT), respectively. Two Convolutional Neural Networks (CNNs) are designed: specifically, the first CNN is trained over a dataset of temporal (T) data (i.e. EEG sources), and is referred to as T-CNN; the second CNN is trained over a dataset of TF data (i.e. TF-maps of EEG sources), and is referred to as TF-CNN. Two sets of features denoted as T-features and TF-features, extracted from T-CNN and TF-CNN, respectively, are concatenated in a single features vector (denoted as TTF-features vector) which is used as input to a standard multi-layer perceptron for classification purposes. Experimental results show a significant performance improvement of our proposed hybrid-domain DL approach as compared to temporal-only and time-frequency-only-based benchmark approaches, achieving an average accuracy of 7 6. 2 1 ± 3. 7 7 %. [ABSTRACT FROM AUTHOR]

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer's Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC. [ABSTRACT FROM AUTHOR]

A complex network approach is combined with time dynamics in order to conduct a space-time analysis applicable to longitudinal studies aimed to characterize the progression of Alzheimer's disease (AD) in individual patients. The network analysis reveals how patient-specific patterns are associated with disease progression, also capturing the widespread effect of local disruptions. This longitudinal study is carried out on resting electroence phalography (EEGs) of seven AD patients. The test is repeated after a three months' period. The proposed methodology allows to extract some averaged information and regularities on the patients' cohort and to quantify concisely the disease evolution. From the functional viewpoint, the progression of AD is shown to be characterized by a loss of connected areas here measured in terms of network parameters (characteristic path length, clustering coefficient, global efficiency, degree of connectivity and connectivity density). The differences found between baseline and at follow-up are statistically significant. Finally, an original topographic multiscale approach is proposed that yields additional results. [ABSTRACT FROM AUTHOR]

An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer's disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database. [ABSTRACT FROM AUTHOR]

Abstract: Artifacts are disturbances that may occur during signal acquisition and may affect their processing. The aim of this paper is to propose a technique for automatically detecting artifacts from the electroencephalographic (EEG) recordings. In particular, a technique based on both Independent Component Analysis (ICA) to extract artifactual signals and on Renyi’s entropy to automatically detect them is presented. This technique is compared to the widely known approach based on ICA and the joint use of kurtosis and Shannon’s entropy. The novel processing technique is shown to detect on average 92.6% of the artifactual signals against the average 68.7% of the previous technique on the studied available database. Moreover, Renyi’s entropy is shown to be able to detect muscle and very low frequency activity as well as to discriminate them from other kinds of artifacts. In order to achieve an efficient rejection of the artifacts while minimizing the information loss, future efforts will be devoted to the improvement of blind artifact separation from EEG in order to ensure a very efficient isolation of the artifactual activity from any signals deriving from other brain tasks. [Copyright &y& Elsevier]

Most clinical measurements rely on bioelectromagnetic phenomena. These events allow us to record electric or magnetic signals during the activity of living tissues. In this paper, we put our attention on the bioelectric fields that occur in the muscle activity. In fact during the body movements, the muscle contractions produce a bioelectric potential distribution that can be measured by putting the electrodes on the skin. In clinical applications, the monitoring of muscle activity can be performed in a noninvasive way, by placing a fixed number of electrodes on the skin surface; this technique is called surface electromyography (sEMG), and it is able to reveal the electric field generated by each muscle activity. Unfortunately, the sEMG suffers from the external electromagnetic fields. In recent years, many authors investigated the correlation between muscle and brain activity by performing the sEMG during the functional magnetic resonance imaging (fMRI). The fMRI is the most reliable technique to evaluate the brain activity because it allows us to obtain some images of the human body with very high resolutions. Unfortunately, joint measurement is a very difficult task because of the high electromagnetic interference between the resonance coils (very high magnetic fields) and the sEMG electrodes. In this paper, we present a method based on wavelet analysis to reveal sEMG in voluntary contractions when the measurement is made in a fMRI environment. [ABSTRACT FROM AUTHOR]

Presents a study that developed a method for detecting and classifying defects in metallic plates. Background on nondestructive testing in the field of defect identification; Analysis of fuzzy entropy in measuring the depth of defects; Results and implications.

Presents a study that applied a fuzzy time series approach to the detection of disruption in Tokamak reactors. Background on the incidence of disruptions in Tokamaks; Analysis of the effectiveness of the disruption prediction approach; Results and implications.

This paper focuses on the processing of experimentally measured pollution data. Measuring locally both air quality parameters and atmospheric data can show how complex can be their interrelations and how they change spatially. Furthermore, apart from physical and biochemical dependencies, two important aspects need to be incorporated in the model, traffic data and topographic information, like presence and configuration of buildings and roads. Since estimating the evolution of pollutant in the urban air can have significant economic impact already on a short term basis as well as relevant consequences on public health on a medium-long term scale, various interdisciplinary researches are under way on this subject. In this work, we pursue two goals. The first one is to derive a representative model of the multivariate relationships that should be able to reproduce local interactions; the second goal of the paper is to predict, when possible, the short term evolution of pollutants in order to prevent the onset of above threshold levels of pollutants that can be dangerous to humans. The threshold levels of interest are fixed by both EU recommendations and regional regulations. As a by-product of the research, we could derive some directives to be supplied to local authorities to properly organize car traffic in advance based on the estimated parameters. The case study here proposed is that of Villa San Giovanni, a small town at the tip of Italy, located just in front of Sicily, on the Messina Strait. This is a significant case, since the city is affected by the heavy traffic directed (and coming from) Sicily. The main results here reported include the short time prediction of the concentration of hydrocarbons (HC) in the local air, the comparison between different methods based on fuzzy neural systems, and the proposal of local models of non-linear interactions among traffic, atmospheric and pollution data. Additionally, comments on a longer horizon forecast are given. [Copyright &y& Elsevier]

Presents a study which examined a neural-based signal processing system that exploits radial basis function neural network (RBFNN) for detecting and locating circular holes in conducting plates. Theoretical background; Methods used; Findings and discussion.

Proposes a neural network model to treat inverse problems in electromagnetics which includes wavelet functions to improve local approximation capabilities. Wavelet approach; Novel combined processor; Details on test case.

In the application areas of streaming, social networks, and video-sharing platforms such as YouTube and Facebook, along with traditional television systems, programs’ classification stands as a pivotal effort in multimedia content management. Despite recent advancements, it remains a scientific challenge for researchers. This paper proposes a novel approach for television monitoring systems and the classification of extended video content. In particular, it presents two distinct techniques for program classification. The first one leverages a framework integrating Structural Similarity Index Measurement and Convolutional Neural Network, which pipelines on stacked frames to classify program initiation, conclusion, and contents. Noteworthy, this versatile method can be seamlessly adapted across various systems. The second analyzed framework implies directly processing optical flow. Building upon a shot-boundary detection technique, it incorporates background subtraction to adaptively discern frame alterations. These alterations are subsequently categorized through the integration of a Transformers network, showcasing a potential advancement in program classification methodology. A comprehensive overview of the promising experimental results yielded by the two techniques is reported. The first technique achieved an accuracy of 95%, while the second one surpassed it with an even higher accuracy of 87% on multiclass classification. These results underscore the effectiveness and reliability of the proposed frameworks, and pave the way for a more efficient and precise content management in the ever-evolving landscape of multimedia platforms and streaming services. [ABSTRACT FROM AUTHOR]

In this study, a few-shot transfer learning approach was introduced to decode movement intention from electroencephalographic (EEG) signals, allowing to recognize new tasks with minimal adaptation. To this end, a dataset of EEG signals recorded during the preparation of complex sub-movements was created from a publicly available data collection. The dataset was divided into two parts: the source domain dataset (including 5 classes) and the support (target domain) dataset, (including 2 classes) with no overlap between the two datasets in terms of classes. The proposed methodology consists in projecting EEG signals into the space-frequency-time domain, in processing such projections (rearranged in channels × frequency frames) by means of a custom EEG-based deep neural network (denoted as EEGframeNET5), and then adapting the system to recognize new tasks through a few-shot transfer learning approach. The proposed method achieved an average accuracy of 72.45 ± 4.19% in the 5-way classification of samples from the source domain dataset, outperforming comparable studies in the literature. In the second phase of the study, a few-shot transfer learning approach was proposed to adapt the neural system and make it able to recognize new tasks in the support dataset. The results demonstrated the system's ability to adapt and recognize new tasks with an average accuracy of 80 ± 0.12% in discriminating hand opening/closing preparation and outperforming reported results in the literature. This study suggests the effectiveness of EEG in capturing information related to the motor preparation of complex movements, potentially paving the way for BCI systems based on motion planning decoding. The proposed methodology could be straightforwardly extended to advanced EEG signal processing in other scenarios, such as motor imagery or neural disorder classification. [ABSTRACT FROM AUTHOR]

In the current digital era, one of the most critical and challenging issues is ensuring cybersecurity in information technology (IT) infrastructures. With significant improvements in technology, hackers have been developing ever more complex and dangerous malware attacks that make intrusion recognition a very difficult task. In this context, traditional analytical tools are facing severe challenges to detect and mitigate these threats. In this work, we introduce a novel statistical analysis and autoencoder (AE) driven intelligent intrusion detection system (IDS). Specifically, the proposed IDS combines data analytics and statistical techniques with recent advances in machine learning theory to extract more optimized, strongly correlated features. The proposed IDS is evaluated using the benchmark NSL-KDD database. Comparative experimental results show that the designed statistical analysis and AE based IDS achieves better classification performance compared to conventional deep and shallow machine learning and other recently proposed state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In this paper, we propose a network analysis-based approach to help experts in their analyses of subjects with mild cognitive impairment (hereafter, MCI) and Alzheimer's disease (hereafter, AD) and to investigate the evolution of these subjects over time. The inputs of our approach are the electroencephalograms (hereafter, EEGs) of the patients to analyze, performed at a certain time and, again, 3 months later. Given an EEG of a subject, our approach constructs a network with nodes that represent the electrodes and edges that denote connections between electrodes. Then, it applies several network-based techniques allowing the investigation of subjects with MCI and AD and the analysis of their evolution over time. (i) A connection coefficient, supporting experts to distinguish patients with MCI from patients with AD; (ii) A conversion coefficient, supporting experts to verify if a subject with MCI is converting to AD; (iii) Some network motifs, i.e., network patterns very frequent in one kind of patient and absent, or very rare, in the other. Patients with AD, just by the very nature of their condition, cannot be forced to stay motionless while undergoing examinations for a long time. EEG is a non-invasive examination that can be easily done on them. Since AD and MCI, if prodromal to AD, are associated with a loss of cortical connections, the adoption of network analysis appears suitable to investigate the effects of the progression of the disease on EEG. This paper confirms the suitability of this idea Graphical Abstract Ability of our proposed model to distinguish a control subject from a patient with MCI and a patient with AD. Blue edges represent strong connections among the corresponding brain areas; red edges denote middle connections, whereas green edges indicate weak connections. In the control subject (at the top), most connections are blue. In the patient with MCI (at the middle), most connections are red and green. In the patient with AD (at the bottom), most connections are either absent or green. . [ABSTRACT FROM AUTHOR]

This work is devoted to solve the problem of the determination of the spatial position, orientation and size of an electrically charged ellipsoidal conductor subjected to an external electric field. The problem, which is relevant to the field of the non destructive testing and evaluations engineering, is approached in fuzzy domain replacing it in an equivalent classification problem between images through the use of the fuzzy divergence concept. The obtained numerical results demonstrate how the performances of the proposed approach are totally comparable with those given by the more sophisticate and well established fuzzy clustering techniques exploited in literature, offering thus a viable alternative to solve identification problem in electrostatics. [ABSTRACT FROM AUTHOR]

This paper proposes a generative model and transfer learning powered system for classification of Scanning Electron Microscope (SEM) images of defective nanofibers (D-NF) and nondefective nanofibers (ND-NF) produced by electrospinning (ES) process. Specifically, a conditional-Generative Adversarial Network (c-GAN) is developed to generate synthetic D-NF/ND-NF SEM images. A transfer learning-oriented strategy is also proposed. First, a Convolutional Neural Network (CNN) is pre-trained on real images. The transfer-learned CNN is trained on synthetic SEM images and validated on real ones, reporting accuracy rate up to 95.31%. The achieved encouraging results endorse the use of the proposed generative model in industrial applications as it could reduce the number of needed laboratory ES experiments that are costly and time consuming. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR) have been an important imaging modality for assisting in the diagnosis and management of hospitalised Covid-19 patients. However, their interpretation is time intensive for radiologists. Accurate computer aided systems can facilitate early diagnosis of Covid-19 and effective triaging. In this paper, we propose a fuzzy logic based deep learning (DL) approach to differentiate between CXR images of patients with Covid-19 pneumonia and with interstitial pneumonias not related to Covid-19. The developed model here, referred to as CovNNet , is used to extract some relevant features from CXR images, combined with fuzzy images generated by a fuzzy edge detection algorithm. Experimental results show that using a combination of CXR and fuzzy features, within a deep learning approach by developing a deep network inputed to a Multilayer Perceptron (MLP), results in a higher classification performance (accuracy rate up to 81%), compared to benchmark deep learning approaches. The approach has been validated through additional datasets which are continously generated due to the spread of the virus and would help triage patients in acute settings. A permutation analysis is carried out, and a simple occlusion methodology for explaining decisions is also proposed. The proposed pipeline can be easily embedded into present clinical decision support systems. [ABSTRACT FROM AUTHOR]

In this study, an accurate analytic semi-linear elliptic differential model for a circular membrane MEMS device, which considers the effect of the fringing field on the membrane curvature recovering, is presented. A novel algebraic condition, related to the membrane electromechanical properties, able to govern the uniqueness of the solution, is also demonstrated. Numerical results for the membrane profile, obtained by using the Shooting techniques, the Keller–Box scheme, and the III/IV Stage Lobatto IIIa formulas, have been carried out, and their performances have been compared. The convergence conditions, and the possible presence of ghost solutions, have been evaluated and discussed. Finally, a practical criterion for choosing the membrane material as a function of the MEMS specific application is presented. [ABSTRACT FROM AUTHOR]

Production of carbon fiber reinforced polymers (i.e., one of the basic material of the modern airplanes) is an elaborate process unfree from faults and problems. Errors during the manufacturing or the plies' overlapping, in fact, can cause particular flaws in the resulting material, so compromising its same integrity. Within this framework, ultrasonic tests could be useful to characterize the presence of defect, depending on its dimensions. On the contrary, the requirement of a perfect state for used polymers is unavoidable in order to assure both transport reliability and passenger safety. Therefore, a real-time approach able to recognize and classify the defect starting from the measured ultrasonic echoes could be very useful in industrial applications. The ill-posedness of the so defined process induce a regularization method. In this paper, an heuristic approach is proposed for this aim. Particularly, the proposed method is based on the use of support vector machines. Obtained results assure good performances of the implemented classifier, with very interesting applications. [ABSTRACT FROM AUTHOR]

Abstract: The up-to-date structural designing makes by now widely use of high performance numerical codes, mainly in terms of computational powerful, cost and sizing, only available till some time before to limited groups of users. This allowed the experts to focus their attention on a qualifying aspect of the designing, i.e. an use of the materials very close to their limit behavior. Late innovative approaches in material mechanics gave in addition the opportunity to build models very close to the actual behavior but without introducing heavy computational aspects. In this paper, phenomena which relate mechanical stresses with electromagnetic properties of a defined material have been exploited in order to reconstruct electromagnetic maps starting from mechanical quantities by means of support vector regression machines (SVRMs). Purpose of the proposed study is to reconstruct a stress map in strained metallic plates by using electromagnetic measures. Moreover, an heuristic approach is proposed in order to estimate electromagnetic behavior of a stressed plate starting from easily measurable mechanical quantities. It would be very interesting when electrical or mechanical measurements are very hard to realize. The proposed approach could be very useful in such situations as quality controls of civil buildings, without the necessity of applying expensive and time-consuming destructive or non-destructive testing. In this way, it is possible to have a substantially precise idea of mechanical stresses in metallic materials by estimating the local variation of electromagnetic field into the same material using a SVRM-based interpolator. [Copyright &y& Elsevier]

The evolution of engineering applications is increasingly shifting towards the embedded nature, resulting in low-cost solutions, micro/nano dimensional and actuators being exploited as fundamental components to connect the physical nature of information with the abstract one, which is represented in the logical form in a machine. In this context, the scientific community has gained interest in modeling membrane Micro-Electro-Mechanical-Systems (MEMS), leading to a wide diffusion on an industrial level owing to their ease of modeling and realization. Physically, once the external voltage is applied, an electrostatic field, orthogonal to the tangent line of the membrane, is established inside the device, producing an electrostatic pressure that acts on the membrane, deforming it. Evidently, the greater the amplitude of the electrostatic field is, the greater the curvature of the membrane. Thus, it seems natural to consider the amplitude of the electrostatic field proportional to the curvature of the membrane. Starting with this principle, the authors are actively involved in developing a second-order semi-linear elliptic model in 1D and 2D geometries, obtaining important results regarding the existence, uniqueness and stability of solutions as well as evaluating the particular operating conditions of use of membrane MEMS devices. In this context, the idea of providing a survey matures to discussing the similarities and differences between the analytical and numerical results in detail, thereby supporting the choice of certain membrane MEMS devices according to the industrial application. Finally, some original results about the stability of the membrane in 2D geometry are presented and discussed. [ABSTRACT FROM AUTHOR]

Creutzfeldt-Jacob disease (CJD) is a rapidly progressive, uniformly fatal transmissible spongiform encephalopathy. Sporadic CJD (sCJD) is the most common form of CJD. Electroencephalography (EEG) is one of the main methods to perform clinical diagnosis of CJD, mainly because of periodic sharp wave complexes (PSWCs). In this paper, we propose a network analysis based approach to characterizing PSWCs in EEGs of patients with sCJD. Our approach associates a network with each EEG at disposal and defines a new numerical coefficient and some network motifs, which characterize the presence of PSWCs in an EEG tracing. The new coefficient, called connection coefficient, and the detected network motifs are capable of characterizing the EEG tracing segments with PSWCs. Furthermore, network motifs are able to detect what are the most active and/or connected brain areas in the tracing segments with PSWCs. The results obtained show that, analogously to what happens for other neurological diseases, network analysis can be successfully exploited to investigate sCJD. [ABSTRACT FROM AUTHOR]

The use of a deep neural network scheme is proposed to help clinicians solve a difficult diagnosis problem in neurology. The proposed multilayer architecture includes a feature engineering step (from time-frequency transformation), a double compressing stage trained by unsupervised learning, and a classification stage trained by supervised learning. After fine-tuning, the deep network is able to discriminate well the class of patients from controls with around 90% sensitivity and specificity. This deep model gives better classification performance than some other standard discriminative learning algorithms. As in clinical problems there is a need for explaining decisions, an effort has been carried out to qualitatively justify the classification results. The main novelty of this paper is indeed to give an entropic interpretation of how the deep scheme works and reach the final decision. [ABSTRACT FROM AUTHOR]

In the study of neurological disorders, Electroencephalographic (EEG) signal processing can provide valuable information because abnormalities in the interaction between neuron circuits may reflect on macroscopic abnormalities in the electrical potentials that can be detected on the scalp. A Mild Cognitive Impairment (MCI) condition, when caused by a disorder degenerating into dementia, affects the brain connectivity. Motivated by the promising results achieved through the recently developed descriptor of coupling strength between EEG signals, the Permutation Disalignment Index (PDI), the present paper introduces a novel PDI-based complex network model to evaluate the longitudinal variations in brain-electrical connectivity. A group of 33 amnestic MCI subjects was enrolled and followed-up with over four months. The results were compared to MoCA (Montreal Cognitive Assessment) tests, which scores the cognitive abilities of the patient. A significant negative correlation could be observed between MoCA variation and the characteristic path length (λ) variation (r = -0.56, p = 0.0006), whereas a significant positive correlation could be observed between MoCA variation and the variation of clustering coefficient (CC, r = 0.58, p = 0.0004), global efficiency (GE, r = 0.57, p = 0.0005) and small worldness (SW, r = 0.57, p = 0.0005). Cognitive decline thus seems to reflect an underlying cortical "disconnection" phenomenon: worsened subjects indeed showed an increased l and decreased CC, GE and SW. The PDI-based connectivity model, proposed in the present work, could be a novel tool for the objective quantification of longitudinal brain-electrical connectivity changes in MCI subjects. [ABSTRACT FROM AUTHOR]

Highlights: [•] We evaluated permutation entropy (PE) extracted from different electrodes in patients with typical absences (TAs) and healthy subjects. [•] In patients with TAs there was a recurrent behavior of PE topography, with electrodes from anterior regions associated to higher randomness and electrodes from posterior regions associated to lower randomness. [•] PE seems a useful tool to disclose abnormalities of cerebral electric activity not revealed by conventional EEG. [Copyright &y& Elsevier]

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses. [ABSTRACT FROM AUTHOR]

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects. [ABSTRACT FROM AUTHOR]

The release of petroleum liquids in water, such as marine, riverine, and lacustrine basins, is a matter of concern that shoves authorities and experts to adopt technical approaches for preventing damages, monitoring oil content in water and cleaning up environmental aqueous matrices. In this paper, a dedicated system for oil spill detection is presented. The proposed system consists of an optical fiber sensor and an image processing unit useful to steer and optimize the measurement process. An optical fiber-based antenna sensor is used to detect the oil concentration in water. The sensor consists of two slanted optical probes acting as transmitter and receiver, respectively. Both probes are completely immersed into water being analyzed. The sensing approach is based on the measurement of the light coupling level affected by the reflectivity of the oil layer floating on the water surface. The experimental measurement of different types of oil is performed to assess the sensitivity of the developed system. Special attention is put on the image postelaboration useful to derive the characteristics of the oil distribution on the water surface. In this respect, two different image processing techniques are considered: the first one is based on a suitable energy-minimizing spline fitting procedure subject to external constraint forces, whereas a judicious use of the Hough transform is made in the second one. [ABSTRACT FROM PUBLISHER]

Abstract: Real-time non-destructive testing and evaluation (NDT/E) of conducting materials using eddy current techniques (ECTs) has gained significance in the last few years. This paper proposes a real-time application of ECT–NDT system exploiting giant magneto-resistive (GMR) sensors for inspection of printed circuit boards (PCBs). Probe design aims to crack inspection over flat surface, especially suitable for micro-defect detection on high density bare PCB. We propose a system based on a GMR sensor able to detect the magnetic field resulting from the interaction between a planar coil exciter and PCBs. The EC signals, detected by the GMR sensor, have been acquired by a high speed analog-to-digital (A/D) converter, for a subsequent application of signal processing based on digital techniques. The achieved results have highlighted the efficient design of the system. The advantages of the proposed models and some possible improvements of the system are also discussed. [Copyright &y& Elsevier]

In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emotion recognition, partcularly in distress identification due to its negative impact on physical and mental health. Traditionally, brain activity has been studied from a frequency perspective by computing the power spectral density of the EEG recordings and extracting features from different frequency sub-bands. However, these features are often individually extracted from single EEG channels, such that each brain region is separately evaluated, even when it has been corroborated that mental processes are based on the coordination of different brain areas working simultaneously. To take advantage of the brain's behaviour as a synchronized network, in the present work, 2-D and 3-D spectral images constructed from common 32 channel EEG signals are evaluated for the first time to discern between emotional states of calm and distress using a well-known deep-learning algorithm, such as AlexNet. The obtained results revealed a significant improvement in the classification performance regarding previous works, reaching an accuracy about 84%. Moreover, no significant differences between the results provided by the diverse approaches considered to reconstruct 2-D and 3-D spectral maps from the original location of the EEG channels over the scalp were noticed, thus suggesting that these kinds of images preserve original spatial brain information. [ABSTRACT FROM AUTHOR]

Even though individual mental and health status largely affects the safety in industrial sites, most studies for preventing industrial accidents are mainly focused on external factors such as regulations, education, etc. In this study, the effect of individual factors on safety (i.e., safety consciousness and safety commitment) was analyzed by collecting brainwave and pulse data at construction sites where industrial accidents have occurred with the highest percentage. The effects of brain stress, concentration, brain activity, and left and right brain imbalance on safety accidents were evaluated through brain wave measurements. In addition, the effects of cumulative fatigue, physical vitality, autonomic nerve health, and autonomic balance were identified through pulse wave measurements. Data were acquired for 180 construction workers at various construction sites, and the workers were classified into three grades according to factors that affected safety accidents at construction sites. Then, the safety consciousness and safety commitment levels of workers corresponding to each grade of the influence factors were evaluated by conducting a questionnaire on safety consciousness and safety commitment. As a result, the characteristics of brain and pulse waves required to improve safety consciousness and safety commitment ability of workers at construction sites were explored. [ABSTRACT FROM AUTHOR]

Electroencephalographic (EEG) recordings can be of great help in decoding the open/close hand’s motion preparation. To this end, cortical EEG source signals in the motor cortex (evaluated in the 1-s window preceding movement onset) are extracted by solving inverse problem through beamforming. EEG sources epochs are used as source-time maps input to a custom deep convolutional neural network (CNN) that is trained to perform 2-ways classification tasks: pre-hand close (HC) versus resting state (RE) and pre-hand open (HO) versus RE. The developed deep CNN works well (accuracy rates up to 89.65±5.29%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$89.65 \pm 5.29\%$$\end{document} for HC versus RE and 90.50±5.35%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$90.50 \pm 5.35\%$$\end{document} for HO versus RE), but the core of the present study was to explore the interpretability of the deep CNN to provide further insights into the activation mechanism of cortical sources during the preparation of hands’ sub-movements. Specifically, occlusion sensitivity analysis was carried out to investigate which cortical areas are more relevant in the classification procedure. Experimental results show a recurrent trend of spatial cortical activation across subjects. In particular, the central region (close to the longitudinal fissure) and the right temporal zone of the premotor together with the primary motor cortex appear to be primarily involved. Such findings encourage an in-depth study of cortical areas that seem to play a key role in hand’s open/close preparation. [ABSTRACT FROM AUTHOR]

Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field. [ABSTRACT FROM AUTHOR]

Monitoring healthcare providers' cognitive workload during surgical procedures can provide insight into the dynamic changes of mental states that may affect patient clinical outcomes. The role of cognitive factors influencing both technical and non-technical skill are increasingly being recognized, especially as the opportunities to unobtrusively collect accurate and sensitive data are improving. Applying sensors to capture these data in a complex real-world setting such as the cardiac surgery operating room, however, is accompanied by myriad social, physical, and procedural constraints. The goal of this study was to investigate the feasibility of overcoming logistical barriers in order to effectively collect multi-modal psychophysiological inputs via heart rate (HR) and near-infrared spectroscopy (NIRS) acquisition in the real-world setting of the operating room. The surgeon was outfitted with HR and NIRS sensors during aortic valve surgery, and validation analysis was performed to detect the influence of intra-operative events on cardiovascular and prefrontal cortex changes. Signals collected were significantly correlated and noted intra-operative events and subjective self-reports coincided with observable correlations among cardiovascular and cerebral activity across surgical phases. The primary novelty and contribution of this work is in demonstrating the feasibility of collecting continuous sensor data from a surgical team member in a real-world setting. [ABSTRACT FROM AUTHOR]