Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) are two of the most common neurodegenerative disorders, most commonly leading to progressive cognitive decline. Electroencephalography (EEG) has been proven as a promising tool for early and non-invasive detection of these disorders, leveraging advanced computational methods for classification. In this study, we propose a novel Convolutional Neural Network (CNN) architecture for the automatic classification of AD and FTD using EEG-based biomarkers. Our approach utilizes Power Spectral Density derived Relative Band Power and Spectral Coherence Index as input features, extracted from preprocessed EEG signals of 88 participants following Independent Component Analysis and Artifact Subspace Reconstruction for noise reduction. The extracted features capture both spectral power variations and functional connectivity disruptions, key findings of neurodegenerative disorders. The proposed CNN model is trained and validated using a Leave-One-Subject-Out crossvalidation approach to ensure robust generalization. Experimental results demonstrate that our model achieves high classification accuracy, outperforming state-of-the-art machine learning methods and achieving competitive performance with state-of-the-art deep learning approaches. These findings highlight the effectiveness of EEG-based deep learning models for differential dementia diagnosis, providing a potential avenue for early detection and clinical decision support.

This preliminary study investigates the effects of sleep deprivation on driving performance and evaluates three gamified or stimulating interventions: airflow, music, and caffeine. Two participants completed simulator-based driving tasks in rested and sleep-deprived states. Performance was assessed via lane deviation (SDLP), and EEG data were analyzed. Sleep deprivation increased SDLP and low-frequency EEG activity, indicating reduced alertness. Caffeine showed the strongest restorative effect. Despite the small sample, results suggest EEG is useful for detecting fatigue, and gamified stimuli may support alertness during drowsy driving.

In this study, we present a novel approach to address the task of identifying nuclei in a diverse set of microscopy scan images containing slides from several distinct cancerous tissue types. Our approach is based on YOLOv5 algorithm. We thoroughly discuss the crucial training specifics that contributed to our outcomes. Additionally, we propose an intuitive strategy for combining multiple segmentation results to further improve the accuracy of nucleus identification. By employing this lightweight model, we successfully achieved results better or close to the state-of-the-art performance.

The Deep-in-Biopsies (DiB) platform supports diagnosing and staging diseases using patient biopsy imaging. This flexible service is designed for medical units to handle multiple organs and diseases. Specialists can train the system on demand using their selected images and annotations, which can be generated using an embedded annotation tool. The system proposes pathology findings to the expert for validation, and validated findings can be used for further training.

Alzheimer's disease and Frontotemporal dementia are the two most reported dementia cases. They both are neurodegenerative disorders without cure while existing treatments only halt their progress. Thus, early detection is of crucial importance. In this work, we utilize electroencephalographic signals of AD and FTD patients and propose a classification pipeline to distinguish them from healthy signals. This pipeline consists of Independent Component Analysis as a preprocessing stage, the extraction of time, frequency and complexity features, feature elimination through importance ranking and finally classification through utilizing Gradient Boosting Decision Trees. The proposed methodology achieved 92.27% F1 score in the Dementia versus Control problem, 83.06% in the AD versus Control and 80.67% in the FTD versus Control.

In the real world, consensus achievement is an adaptive and evolutionary process. Tailor-made mechanisms are engaging in the light of disputes to resolve emerging contradictions between the atoms. Most of the dominant blockchain architectures tend to oversee this fact. They come integrated with rigid, overkilling consensus-achievement procedures, which, in many cases, are not necessary or significantly beneficial. They often raise overwhelming demands on the nodes and prohibit the deployment of fully distributed and peer blockchain operation in the IoT world. Still, for the IoT ecosystems and the metaverse to become self-sustainable and self-evolving, robust validity mechanisms must be embedded in the atomic scale. This work investigates the process of building and sustaining scalable consensus policies over the trivial atomic capacities of the IoT ecosystems. For this, we deploy the IoT microblockchain framework as an atomic consistency tier and we define a primary set of validity rules that every node can easily carry out. The initial concept relies on Gödel’s incompleteness theorems and K. Friston’s free energy principle and is investigated under the perspectives of graph and game theory. To study the dynamic behavior of the system, a competitive game among the nodes is run. The Proof of Existence (PoE) is utilized as a universal proofing case. Its feasibility is demonstrated, and its complexity is analyzed under various system considerations. The findings prove that finite-capacity atoms can collectively and efficiently support verifiable validity and scalable consensus in the evolutionary IoT ecosystems and the metaverse, even under conditions of high diversity, trivial capacity, and eventual consistency.

Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

In this manuscript, a methodology for analysing motor signals from Parkinson's disease (PD) patients is presented. The signals are obtained from PD patients while wearing a glove device and sequentially performing standard motor tests. The signals are processed in order to detect the onset and offset from specific items (items 23-25) of the Unified Parkinson's Disease Rating Scale (UPDRS) and then the isolated signal parts are analysed in order to quantity the motor findings defined in UPDRS for these items, such as hesitation, movement amplitude and frequency, and rotation range. The obtained results indicate that the methodology can achieve accurate motor assessment (related to ground-truth UPDRS) for both “Off” and “On” stages.

The purpose of fetal monitoring during childbirth is the early recognition of any pathological conditions to guide a clinician in early intervention to avoid any complication in the health of the fetus. Non-Invasive Fetal Electrocardiography (NIFECG) represents an alternative fetal monitoring technique. The fetal ECG (fECG) derived from maternal thoracic and abdominal ECG recordings, provides an alternative to typical embryo monitoring means. In addition, it allows for long-term and ambulatory registrations that broaden the diagnostic capabilities for assessing the fetal health. However, in real situations, clear fECG is difficult to extract because it is usually overwhelmed by the dominant maternal ECG and other contaminated noise such as baseline wander and high-frequency interference. In this paper, a novel integrated adaptive methodology based on the combination of blind source separation, empirical mode decomposition, wavelet shrinkage denoising and correlation analysis, for the non-invasive extraction and processing of the FECG, is proposed. The methodology has been evaluated using both real and simulated recordings, and the obtained results indicate it efficiently.

This paper presents a hybrid algorithm for the creation of heterogeneous single layer neural networks (SLNNs). The proposed self-adaptive heterogeneous hybrid extreme learning machine (SA-He-HyELM) trains a series of SLNNs with different neuron types in the hidden layer utilizing the extreme learning machine (ELM) algorithm. These networks are evolved into heterogeneous networks (networks having different combinations of hidden neurons) with the help of a modified genetic algorithm (GA). The algorithm is able to handle two architecturally different neuron types: traditional low order (linear) units and higher order units with different transfer functions. The GA is fully self-adaptive and uses one novel hybrid crossover operator along with a self-adaptive mutation operator in order to retain ELM's simplicity and minimize the number of parameters need tuning. The experimental part of the current paper involves testing SA-He-HyELM with traditional ELM and other three ELM-based methods. The experimental part utilized a series of regression and classification experiments on relatively large datasets. In all cases the proposed method managed to get lower MSE or higher classification accuracy when compared to the aforementioned methods.

Objective characterization of pain intensity is necessary under certain clinical conditions. The portable electroencephalogram (EEG) is a cost-effective assessment tool and lately, new methods using efficient analysis of related dynamic changes in brain activity in the EEG recordings proved that these can reflect the dynamic changes of pain intensity. In this paper, a novel method for automated assessment of pain intensity using EEG data is presented. EEG recordings from twenty-two (22) healthy volunteers are recorded with the Emotiv EPOC+ using the Cold Pressor Test (CPT) protocol. The relative power of each brain band's energy for each channel is extracted and the stochastic forest algorithm is employed for discrimination across five classes, depicting the pain intensity. Obtained results in terms of classification accuracy reached high levels (72.7%), which renders the proposed method suitable for automated pain detection and quantification of its intensity.

In this paper, a novel stochastic approach for the induction of the decision trees in a tree-structured ensemble classifier is presented. The proposed algorithm is based on a stochastic process to induct each decision tree, assigning a probability for the selection of the split attribute in every tree node, designed in order to create strong and independent trees. A selection of 33 well-known classification datasets have been employed for the evaluation of the proposed algorithm, obtaining high classification results, in terms of Classification Accuracy, Average Sensitivity and Average Precision. Furthermore, a comparative study with Random Forest, Random Subspace and C4.5 is performed. The obtained results indicate the importance of the proposed algorithm, since it achieved the highest overall results in all metrics.

Analysis of histopathological images covers a wide field of clinical practice in different pathological conditions. In many cases, biopsy images from different sources share similar characteristics. In this work, a method for image enhancement of biopsy images is proposed. During the first stage, the quality of the image is optimized, while in the second stage, a clustering technique is employed to separate the tissue from the background. For the evaluation of the method, Liver biopsies which have been extracted for the staging of Hepatitis C, are employed. The methodology has been tested using 19 liver biopsy images from patients who suffer from hepatic fibrosis and steatosis, obtaining detection Accuracy over 97% in pixel level.

In this work, an innovative classification algorithmic technique through sequential pattern mining was developed to predict the secondary structure of proteins. A basic algorithm was selected for the extraction of the sequential patterns and another algorithm was developed which employs these patterns for protein structure prediction. In the matter of predicting protein structures and scoring sequential patterns, several methodologies has been implemented that theoretically and experimentally overcome the disadvantages of existing algorithms.

In this paper, a gamified social platform designed for people living with Dementia (PLWD) and their caregivers is presented. This platform constitutes a support tool that strengthens self-care and builds community capacity and engagement at the point of care. Its architecture related to gamification aspects was designed to be flexible and scalable to support personalized services such as user monitoring, social networking, cognitive skills training and improvement of the adherence to treatment guidelines for PLWD and their caregivers. Advanced visual analytics and reporting tools are available for medical professionals and social workers to support the decision-making processes. The outcomes of this approach are delivered through a set of gamification concepts running in parallel to create motivation for achieving the desired behavioural change. After projecting all user group expectations on a social game canvas, the impact evaluation will assess the intended effects of the proposed gamification approach on the welfare on PLWD and their surroundings.

Non-Alcohol Liver Disease (NAFLD) is nowadays the most common liver disease in Western Countries. It is the chronic condition of fat expansion in liver, which is not associated with alcohol consumption. Quantitating steatosis in liver biopsies could provide objective measurement of the severity of the disease, instead of using semi-quantitative scoring systems. The current work, introduces an automated method for measuring steatosis in liver biopsies, using both machine learning and classical image processing techniques. Clustering is employed for tissue specimen detection, while an iterative morphological procedure is used for steatosis revealing. The method has been evaluated in a set of 20 liver biopsy images and the obtained results present ~1% mean percentage error.

Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity. In current work a novel modification of K-means clustering is proposed for image segmentation of liver biopsies. More specifically, supervised restriction of centroids movement is utilized. In the first stage, a training set of images are employed to extract a hypercube for each class. Then, one centroid is initialized inside each hypercube and during the iterations of the clustering is allowed to move only inside the hypercube. For the evaluation of the proposed method 8 liver biopsy images are employed and classification results along with CPA values are computed for each image.

The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted and a classification algorithm is trained. The training procedure extracts knowledge in terms of a classification model, which is employed in a real-time testing. For the training of the classification scenario a five-classes dataset of EEG signals is employed in which two-classes have been recorded extracranially and the rest three intracranially including one class with epileptic seizure activity and two classes with seizure-free signals. Promising quantitative results are reported.

Epilepsy is a chronic neurological disorder characterized by recurrent, sudden discharges of cerebral neurons, called seizures. Seizures are not always clearly defined and have extremely varied morphologies. Neurophysiologists are not always able to discriminate seizures, especially in long-term EEG datasets. Affecting 1% of the worlds population with 1/3 of the epileptic patients not corresponding to anti-epileptic medication, epilepsy is constantly under the microscope and systems for automated detection of seizures are thoroughly examined. In this paper, a method for automated detection of epileptic activity is presented. The Discrete Wavelet Transform (DWT) is used to decompose the EEG recordings in several subbands and five features are extracted from the wavelet coefficients creating a set of features. The extracted feature vector is used to train a Support Vector Machine (SVM) classifier. Five classification problems are addressed, reaching high levels of overall accuracy ranging from 87% to 100%.

The detection of epileptic seizures is of primary interest for the diagnosis of patients with epilepsy. Epileptic seizure is a phenomenon of rhythmicity discharge for either a focal area or the entire brain and this individual behavior usually lasts from seconds to minutes. The unpredictable and rare occurrences of epileptic seizures make the automated detection of them highly recommended especially in long term EEG recordings. The present work proposes an automated method to detect the epileptic seizures by using an unsupervised method based on k-means clustering end Ensemble Empirical Decomposition (EEMD). EEG segments are obtained from a publicly available dataset and are classified in two categories “seizure” and “non-seizure”. Using EEMD the Marginal Spectrum (MS) of each one of the EEG segments is calculated. The MS is then divided into equal intervals and the averages of these intervals are used as input features for k-Means clustering. The evaluation results are very promising indicating overall accuracy 98% and is comparable with other related studies. An advantage of this method that no training data are used due to the unsupervised nature of k-Means clustering.

Electrooculographic (EOG) artefact is one of the most common contaminations of Electroencephalographic (EEG) recordings. The corruption of EEG characteristics from Blinking Artefacts (BAs) affects the results of EEG signal processing methods and also impairs the visual analysis of EEGs. In this paper, our scope was a comparative analysis of the performance of three standard denoising methods like continuous Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT) and Kalman Filter (KF). In order to evaluate the performance of EMD, DWT and KF of noise reduction and to express the quality of the denoised EEG, we calculate several indexes such as the Signal-to-Noise Ratio (SNR). All the results obtained from noise simulated EEG data show that WT achieved the greatest SNR difference and also the mode mixing issue of EMD affected this method's performance.

In this paper, we introduce a new suction detection approach based on online learning of a Gaussian Mixture Model (GMM) with constrained parameters to model the reduction in pump flow signals baseline during suction events. A novel three-step methodology is employed: i) signal windowing, ii) GMM based classification and iii) GMM parameter adaptation. More specifically, the first 5 second segment is used for the parameter initialization and the consequent 1 second windows are classified and used for model adaptation. The proposed approach has been tested in simulation (pump flow) signals and satisfactory results have been obtained.

Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.

The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency (t-f) analysis to classify EEG segments for epileptic seizures, and we compare several methods for t-f analysis of EEGs. Short-time Fourier transform and several t-f distributions are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: 1) t-f analysis and calculation of the PSD of each EEG segment; 2) feature extraction, measuring the signal segment fractional energy on specific t-f windows; and 3) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks. The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented.

Multiplex fluorescent in situ hybridization (M-FISH) is a recently developed chromosome imaging technique where each chromosome class appears to have a distinct color. This technique not only facilitates the detection of subtle chromosomal aberrations but also makes the analysis of chromosome images easier; both for human inspection and computerized analysis. In this paper, a novel method for segmentation and classification of M-FISH chromosome images is presented. The segmentation is based on the multichannel watershed transform in order to define regions of similar spatial and spectral characteristics. Then, a Bayes classifier, task-specific on region classification, is applied. Our method consists of four basic steps: 1) computation of the gradient magnitude of the image, 2) application of the watershed transform to decompose the image into a set of homogenous regions, 3) classification of each region, and 4) merging of similar adjacent regions. The method is evaluated using a publicly available chromosome image database and the obtained overall accuracy is 82.4%. By introducing the classification of each watershed region, the proposed method achieves substantially better results compared to other methods at a lower computational cost. The combination of the multichannel segmentation and the region-based classification is found to improve the overall classification accuracy compared to pixel-by-pixel approaches.

Multiplex fluorescent in situ hybridization (M-FISH) is a newly chromosome imaging technique where each chromosome class appears to have a distinct color. This technique although it makes the analysis of chromosome images easier, still exhibits misclassification errors that can be misinterpreted as chromosome abnormalities. A new method for the multichannel image segmentation and region classification is proposed. The segmentation of M-FISH images is based on a multichannel watershed segmentation method in order to define regions of same spectral characteristics. The region Bayes classification method which focuses on region classification is used. The classifier was trained and tested on nonoverlapping chromosome images and an overall accuracy 89% is achieved. The superiority of the proposed method over methods that use pixel-by-pixel classification is demonstrated.

A electroencephalographic (EEG) signals, concerning epileptic seizures, is proposed. First, segments of the EEG signals are analyzed using a time-frequency distribution and then, several features are extracted for each segment, representing the energy distribution over the time-frequency plane. Those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments (existence of epileptic seizure or not). The evaluation results are very promising, indicating overall accuracy from 89.4% to 99%.

In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.

An automated methodology which detects transient events in EEG recordings and classifies those as epileptic spikes, muscle activity, eye blinking activity and sharp alpha activity is presented. It is based on data mining algorithms and includes four stages: (I) EEG preprocessing and transient events detection, (II) clustering of transient events and feature extraction, (III) feature discretization and (IV) association rule mining and classification. The methodology is evaluated using a dataset of 25 EEG recordings and the obtained overall accuracy is 84.35%. The major advantage of our approach is that it is able to provide interpretation for the decisions made since it is based on a set of association rules.