Cosimo Ieracitano

Information Theoretic-Based Interpretation of a Deep Neural Network Approach in Diagnosing Psychogenic Non-Epileptic Seizures

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.

A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity

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 λ 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.

Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings

In spite of the worldwide financial and research efforts made, the pathophysiological mechanism at the basis of Alzheimers disease (AD) is still poorly understood. Previous studies using electroencephalography (EEG) have focused on the slowing of oscillatory brain rhythms, coupled with complexity reduction of the corresponding time-series and their enhanced compressibility. These analyses have been typically carried out on single channels. However, limited investigations have focused on the possibility yielded by computational intelligence methodologies and novel machine learning approaches applied to multichannel schemes. The study at screening level on EEG recordings of subjects at risk could be useful to highlight the emergence of underlying AD progression (or at least support any further clinical investigation). In this work, the representational power of Deep Learning on Convolutional Neural Networks (CNN) is exploited to generate suitable sets of features that are then able to classify EEG patterns of AD from a prodromal version of dementia (Mild Cognitive Impairment, MCI) and from age-matched Healthy Controls (HC). The processing system here used enforces a series of convolutional-subsampling layers in order to derive a multivariate assembly of latent, novel patterns, finally used to categorize sets of EEG from different classes of subjects. The final processor here proposed is able to reach an averaged 80% of correct classification with good performance on both sensitivity and specificity by using a Multilayered Feedforward Perceptron (MLP) of the standard type as a final block of the procedure.

Wavelet coherence-based clustering of EEG signals to estimate the brain connectivity in absence epileptic patients

In this paper, the need of novel methods to extract diagnostic information from the Electroencephalographic (EEG) recordings of epileptic patients was addressed. A novel method, based on Wavelet Coherence (WC) between EEG signals and Hierarchical Clustering (HC), was proposed to estimate the EEG network connectivity density in Childhood Absence Epilepsy (CAE) patients. The EEG recordings of four patients affected by CAE were partitioned into non overlapping windows and WC was estimated window by window. The behaviour of WC was analysed over the time, for every couple of EEG electrodes. The ictal states (seizures) resulted associated to increased WC levels, thus reflecting an increased synchronization between electrodes during the seizure. A WC-based dissimilarity index was then defined and HC was fed with the dissimilarity indices between every pair of electrodes with the aim of finding possible correlations between changes in electrode clustering and changes in the brain state. For every window under analysis, a dendrogram was constructed, the corresponding set of electrode clusters was determined and the subsequent network density values were calculated. Seizures resulted typically associated to increased network density, reflecting an increased connectivity during the ictal states.

A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management

Extreme events and disasters resulting from climate change or other ecological factors are difficult to predict and manage. Current limitations of state-of-the-art approaches to disaster prediction and management could be addressed by adopting new unorthodox risk assessment and management strategies. The next generation Internet of Things (IoT), Wireless Sensor Networks (WSNs), 5G wireless communication, and big data analytics technologies are the key enablers for future effective disaster management infrastructures. In this chapter, we commissioned a survey on emerging wireless communication technologies with potential for enhancing disaster prediction, monitoring, and management systems. Challenges, opportunities, and future research trends are highlighted to provide some insight on the potential future work for researchers in this field.

Hierarchical clustering of the electroencephalogram spectral coherence to study the changes in brain connectivity in Alzheimer's disease

Alzheimers disease (AD) is a degenerative neurological disorder characterized by a loss of functional connections between different areas of the brain. AD is considered a cortical dementia, thus Electroencephalography (EEG) has been used as a tool for diagnosing AD for the last two decades. Often, the hallmarks of EEG abnormality in AD patients are a shift of the power spectrum to lower frequencies and reduced coherences among cortical regions, however, it is still mostly unknown how these abnormalities evolve together with the disease progression. In this paper we proposed a longitudinal study of the EEG of three AD patients in order to study the disease progression, from the coherence point of view, over the four major EEG sub-bands: delta, theta, alpha and beta. The EEG was recorded at time T0 and then after three months (time T1). We proposed a coherence based hierarchical clustering method to group the electrodes together according to their mutual pairwise coherence, in order to evaluate how the brain connectivity changed along with the disease in the spectral domain. The results provide an in-depth view of the structure of electrode interconnection of every single patient in every sub-band at time T0 and time T1. This study endorsed the commonly shared belief that coherence reduces over time but it revealed that coherence spatial distribution changes in a different way, from patient to patient. The results also showed that a patient-specific brain connectivity analysis is possible and that a personalized analysis of the diseases progression might provide valuable diagnostic information. In the near future, the study will be extended to a larger dataset in order to validate the method statistically.

Exploiting Deep Learning for Persian Sentiment Analysis

The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as topic, product, movie, news etc. Deep learning has recently emerged as a powerful machine learning technique to tackle a growing demand of accurate sentiment analysis. However, limited work has been conducted to apply deep learning algorithms to languages other than English, such as Persian. In this work, two deep learning models (deep autoencoders and deep convolutional neural networks (CNNs)) are developed and applied to a novel Persian movie reviews dataset. The proposed deep learning models are analyzed and compared with the state-of-the-art shallow multilayer perceptron (MLP) based machine learning model. Simulation results demonstrate the enhanced performance of deep learning over state-of-the-art MLP.

Statistical Analysis Driven Optimized Deep Learning System for Intrusion Detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology (ICT) systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks have made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods, followed by a deep autoencoder (AE) for potential threat detection. Specifically, a preprocessing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discards features with null values grater than 80% and selects the most significant features as input to the deep autoencoder model trained in a greedy-wise manner. The NSL-KDD dataset (an improved version of the original KDD dataset) from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed IDS system for improving intrusion detection as compared to existing state-of-the-art methods.

A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings

Abstract A data-driven machine deep learning approach is proposed for differentiating subjects with Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC), by only analyzing noninvasive scalp EEG recordings. The methodology here proposed consists of evaluating the power spectral density (PSD) of the 19-channels EEG traces and representing the related spectral profiles into 2-d gray scale images (PSD-images). A customized Convolutional Neural Network with one processing module of convolution, Rectified Linear Units (ReLu) and pooling layer (CNN1) is designed to extract from PSD-images some suitable features and to perform the corresponding two and three-ways classification tasks. The resulting CNN is shown to provide better classification performance when compared to more conventional learning machines; indeed, it achieves an average accuracy of 89.8% in binary classification and of 83.3% in three-ways classification. These results encourage the use of deep processing systems (here, an engineered first stage, namely the PSD-image extraction, and a second or multiple CNN stage) in challenging clinical frameworks.

A Neural Network Approach for Predicting the Diameters of Electrospun Polyvinylacetate (PVAc) Nanofibers

This study focuses on the design of a Neural Network (NN) model for the prediction of interpolated values of polyvinylacetate (PVAc) nanofiber diameters produced by the electrospinning process and it supposes to be a preliminary work for future and industrial applications. The experimental data gathered from the literature form the basis for generating a more consistent sample through standard interpolation. The inputs of the NN are the polymer concentration, the applied voltage, the nozzle-collector distance and the flow rate parameters of the process, whereas the average diameter acts as the unique output of the network. The generated model is able to approximate the mapping between process parameters and fiber morphology, which is of practical importance to help prepare homogeneous nano-fibers. The reliability of the model was tested by 7-fold cross validation as well as leave-one-out method, showing good performance in terms of both average RMSE (0.109, corresponding to 138.51 nm) and correlation coefficient (0.905) between the desired and the predicted diameters when a White Gaussian Noise with 2% power (WGN2%) is applied to the interpolations.