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Touch is a fundamental aspect of human interaction with the surrounding environment. It affects individuals' development in different manners and figures prominently in everyday operations such as the sense of presence, object recognition, performing actions, non-verbal communication and emotional state. In recent years there has been a growth of interest in researching the electro physiological activity of the brain originating from haptic stimulation. In the present preliminary experiment, we performed a classification process of extracted EEG features acquired from four healthy participants' EEG data when they actively touched different natural textures. Each participant was asked to use their fingertips and calmly rub for one minute, each of the three different textured materials (smooth, rough and water surface). EEG recordings were acquired and processed. Next, time and frequency-based features were extracted and used as inputto four classifiers to correctly identify each different texture. The results obtained show a classification performance of 63% with C4.5 algorithm and 76% with Random Forests and 10-fold cross-validation.

Non-Alcoholic Fatty Pancreas Disease (NAFPD) is the most common pancreatic condition in adults and is usually associated with obesity and insulin resistance. It is a new medical term that indicates the development of pancreatic steatosis, which at an advanced stage leads to the irreversible replacement of acinar cells with fat droplets. Although increasing prevalence rates are recorded worldwide for this condition, it has been studied to a small extent due to the diagnostic limitations of noninvasive medical imaging methods. In recent years and with the development of modern computer vision systems, digital pathology through biopsy imaging systems has become the gold standard in modern clinical trials. The current work presents an automated diagnostic tool for measuring the fat ratio in pancreatic biopsy specimens. The automated analysis is performed on a set of 20 histological images using supervised machine learning algorithms. Its diagnostic performance presents a minimum fat quantification error of 0.23% compared to that obtained from human semi-quantitative estimates.

In any hospital health care monitoring system, it is necessary to constantly monitor the patient's physiological parameters. Previous studies report that important parameters of any patient that have to be monitored in hospital are heart rate, respiratory rate, oxygen saturation, temperature, change in systolic blood pressure, motion, posture and its location. This work presents a monitoring system that has the capability to monitor in real-time the physiological parameters of the patient using a comfortable wearable device. A Wireless Sensor Network in collaboration with multiple wireless relay nodes, are responsible for collecting and sending the signals from the wireless sensors to the base station. The data is stored and processed using intelligent techniques in a cloud-based environment. Early warning alerts are automatically sent to the medical staff allowing them to intervene on time and earlier than when using manual vital sign observations. By grouping patients according to risk, medical experts can continuously monitor patients health status and quickly identify those that need their attention most.

Continuous patient monitoring during hospitalization is necessary to identify patterns of indicative risks or pathogens, whose early diagnosis and treatment is likely to lead to a reduction in morbidity and mortality and, consequently, a reduction in both the duration and cost of hospitalization. On the other hand, a patient falling from bed can cause serious damage to his health state, while the effect of pressure ulcers can be avoided by timely and accurate mapping of pressure points that inhibit tissue perfusion resulting in death. Recent technological advances and scientific achievements have introduced new and improved medical devices using highly-developed embedded control functions and interactivity. Current hospital beds include new forms of functionality, while still serving the same purpose. All of them are designed to fulfil a predefined purpose, whether that is to monitor the patient's vital signs continuously, in a non-obtrusive manner, or prevent a patient from falling off their bed or prevent the development of pressure ulcers. Over the last decades, this hospital bed evolution has brought a big change in both their standards and the overall patient care. In this work, we conduct a review of existing smart bed systems for patient monitoring, fall and pressure ulcer prevention from the state-of-the-art, focusing on evaluating smart bed systems that include any kind of smart features, like sensors and sensor mats, or exploiting Machine Learning (ML) algorithms and Wireless technology.

In this paper, it is discussed the control of a double inverted pendulum (DIP) on a cart with the use of a Fuzzy Logic Controller (FLC). The DIP is a classic problem in mechanics and the control procedure to stabilize around equilibrium position constitutes a classic criterion for the various control methods in Control Systems Theory and Robotics e.g., can serve as a representative model for the computation and stability of walking robots. To access the efficacy of the proposed method, a linear quadratic regulator (LQR) was used as a benchmark. The study showed that the proposed FLC can provide considerably better performance than LQR under different initial conditions of the system. This performance is remarkably significant in terms of the system outputs peak values reduction. The balancing and velocity tracking performance of the proposed controller was studied via simulations. The simulation result shows the effectiveness of our control scheme.

A new approach for the assessment of motor symptoms caused by Parkinson's disease (PD), based on data recorded using a smart mobile phone, is presented in this manuscript. Data were obtained from the online platform kaggle.com, and were analyzed based on machine learning techniques to produce a comprehensive physician report, presenting motor symptoms in everyday life. The idea of this study is to equip PD patients with a smartphone, which will monitor signals in real time for a period of time in order to enhance the medical diagnosis routine of a physician, providing information about the overall picture of the patient's motor condition, resulting in the provision of individualized treatment to the patient.

This article introduces a method that can automatically classify the hemiplegia type (right or left side of the body is paralyzed) between healthy and non-healthy subjects. The proposed method utilizes the data taken from the accelerometer sensor of the RehaGait mobile gait analysis system. These data undergo a pre-processing and feature extraction stage before being sent as input to a scaled conjugate gradient backpropagation (SCG-BP) trained neural network. The proposed system is tested using a custom-created dataset containing 10 healthy and 20 patients suffering from hemiplegia (right or left). The experimental part of the system utilized 7 sensors placed on the left and right foot, the left and right shank, the left and right thigh, and the hip of each subject. Each sensor captured a 3-dimensional (3D) signal from 3 different device types: accelerometer, magnetometer, and gyroscope. The system utilized and split into 2-second windows only the accelerometer data, achieving a classification accuracy of 87.71%.

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.

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.

Nonalcoholic fatty liver disease (NAFLD) is one of the most frequent liver conditions representing a wide range of intrahepatic disorders, varying from steatosis to nonalcoholic steatohepatitis (NASH). Steatosis refers to the accumulation of benign fat cells, which at higher rates leads to NASH progression, as the major risk factor for hepatic fibrosis and cirrhosis, as well as for hepatocellular carcinoma (HCC). In recent years the medical field has focused on preventing the progression of these diseases, with microscopic biopsy images being the gold standard imaging modality in modern clinical trials. The proposed work aims at the high classification ability of four histological liver structures, by training a convolutional neural network (CNN) and comparing its diagnostic performance with various pre-trained deep CNN architectures. All diagnostic attempts were made on an augmented image dataset, with the new CNN model achieving a 95.8% classification accuracy, while AlexNet emerging as the most efficient architecture with a corresponding performance of 97.8%.

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.

Nonalcoholic fatty liver disease (NAFLD) presents a wide range of pathological conditions, varying from nonalcoholic steatohepatitis (NASH) to cirrhosis and hepatocellular carcinoma (HCC). Their prevalence is characterized by increased fat accumulation and hepatocellular ballooning. They have become a cause of concern among physicians and engineers, as significant implications tend to occur regarding their accurate diagnosis and treatment. Although magnetic resonance, ultrasonography and other noninvasive methods can reveal the presence of NAFLD, image quantitative interpretation through histology has become the gold standard in clinical examinations. The proposed work introduces a fully automated diagnostic tool, taking into account the high discrimination capability of histological findings in liver biopsy images. The developed methodology is based on deep supervised learning and image analysis techniques, with the determination of an efficient convolutional neural network (CNN) architecture, performing eventually a classification accuracy of 95%.

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.

The Decision Support tool of the myAirCoach platform is presented in this work. MyAirCoach is a web-based platform for personalized management of asthma patients. The Decision Support tool is used from medical experts to generate knowledge-based rules for asthma patients. The tool is a web-based application that includes a simple-to-use rule editor for generating medical rules in Arden Syntax, which is an HL7 standard. The logic of the rules in formulated in disjunctive normal form. Using the this tool, medical experts can generate rules and specify the set of rules that are applicable for alerting each patient, thus creating a personalized decision support system for asthma patients.

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.

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%.

A software for distributed neural network training is introduced here. The introduced software named NeuralGenesis implements a client — server model for parallel genetic algorithms with custom features such as: an enhanced stopping rule, an advanced mutation scheme and periodical application of a local search procedure. The software is coded in Qt5 for portability reasons and it is freely available for the majority of operating system.

A state-of-the-art method based on a grammatical evolution approach is utilized in this study to classify EEG signals. The method is able to construct nonlinear mappings of the original features in order to improve their effectiveness when used as input into artificial intelligence techniques. Several features are initially extracted from the EEG signals which are subsequently used to create the non-linear mappings. Then, a classification stage is applied, using multi-layer perceptron (MLP) and radial basis functions (RBF), to categorize the EEG signals. The proposed method is evaluated using a benchmark epileptic EEG dataset and promising results are reported.

PERFORM is a system for the monitoring, assessment and management of patient with Parkinson's disease (PD). It comprises of three subsystems: (i) Multi-Sensor Monitoring Unit, (ii) the Local Base Unit, and (iii) the Centralized Hospital unit. The wearable, multi-sensor monitoring unit (WMSMU) of the PERFORM system is presented in this work. This unit plays an essential role in the overall PERFORM system since it is responsible to record and pre-process accelerometer and gyroscope signals that are later used by the various components of the Local Base Unit in order to classify and quantify the symptoms and motor status of the PD patients. The WMSMU was evaluated in a large set of pilot studies in PD patients.

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.

The SensorART project focus on the management of heart failure (HF) patients which are treated with implantable ventricular assist devices (VADs). This work presents the way that crisp models are transformed into fuzzy in the weaning module, which is one of the core modules of the specialist's decision support system (DSS) in SensorART. The weaning module is a DSS that supports the medical expert on the weaning and remove VAD from the patient decision. Weaning module has been developed following a "mixture of experts" philosophy, with the experts being fuzzy knowledge-based models, automatically generated from initial crisp knowledge-based set of rules and criteria for weaning.

In this work, the weaning module of the SensorART specialist decision support system (SDSS) is presented. SensorART focuses on the treatment of patients suffering from end-stage heart failure (HF). The use of a ventricular assist device (VAD) is the main treatment for HF patients. However in certain cases, myocardial function recovers and VADs can be explanted after the patient is weaned. In that framework an efficient module is developed responsible for the selection of the most suitable candidates for VAD weaning. In this study we describe all technical specifications concerning its two main sub-modules of the weaning module, of the Clinical Knowledge Editor and the Knowledge Execution Engine.

The platform of SensorART emphases on the controlling and remote treatment of patients suffering from failure of the heart, using an implantable Ventricular Assist Device (VAD). It provides an extendable, interoperable and independent from VAD solution, which includes different software and hardware components in a holistic approach, in order to improve the quality the treatment of the patients and the workflow of the medical experts. In this work, we present one of the core modules of the SensorART platform, the specialist's decision support system (SDSS) which assists health professionals in designing the best treatment strategy. The SDSS supports the specialists on deciding the best treatment strategy for a VAD patient. It has the following functionalities: a) Selection of the most suitable candidates for VAD weaning, b) Decision of the most appropriate treatment strategy for the medication process, c) Analysis of patients' data and the extraction of new knowledge, d) Detection of different pump states and the identification possible issues associated to the suction phenomenon and e) Identification of the most proper pump speed settings. The SDSS combines medical knowledge with efficient data-driven techniques, using a design that is based on approved principles and standards that ensure the avoidance of either business or technical barriers [1].

In this work a decision support tool for optimal administration of Levodopa (L-Dopa) in patients with Parkinson's Disease (PD) is presented. The so called Medication Change Proposer (MCP) is part of the PERFORM system, which monitors patient's motion status using a set of wearable sensors, assesses PD symptoms and their severity through dedicated symptoms detection modules (On-Off detection and Levodopa-Induced Dyskinesia detection), and proposes treatment plans to alleviate the symptoms. The methodology is based on the analysis of the outputs of the symptoms detection modules, taking into account the uncertainty (confidence) propagated by their respective classification algorithms. The evaluation of our methodology is performed using artificial follow-up data produced by a specially built simulation environment. The obtained results demonstrate that the MCP outputs compare well with the experts' decisions, indicating high performance.

A novel three-stage methodology for the detection of fetal heart rate (fHR) from multivariate abdominal electrocardiogram (ECG) recordings is introduced. In the first stage, the maternal R-peaks and fiducial points (maternal QRS onset and offset) are detected. Maternal fiducial points are used to eliminate the maternal QRS complexes from the abdominal ECG recordings. In the second stage, two denoising procedures are applied to enhance the fetal QRS complexes. The phase space characteristics are employed to identify fetal heart beats not overlapping with the maternal QRSs which are eliminated in the first stage. The extraction of the fetal heart rate is accomplished in the third stage, using a histogram based technique in order to identify the location of the fetal heart beats which overlap with the maternal QRSs. The methodology is evaluated on simulated and real multichannel ECG signals. In both cases, the obtained results indicate high performance; in the simulated ECGs the accuracy ranges from 74.21-100%, depending on the employed SNR, while in the real recordings the average accuracy is 94.08%.

In this paper we present a methodology for biosequence classification, which employs sequential pattern mining and optimization algorithms. In the first stage, a sequential pattern mining algorithm is applied to a set of biological sequences and the sequential patterns are extracted. Then, the score of each pattern with respect to each sequence is calculated using a scoring function and the score of each class under consideration is estimated. The scores of the patterns and classes are updated, multiplied by a weight. In the second stage an optimization technique is employed to calculate the weight values to achieve the optimal classification accuracy. The methodology is applied in the protein class and fold prediction problem. Extensive evaluation is carried out, using a dataset obtained from the Protein Data Bank.

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%.

A three-stage method for fetal heart rate extraction, from abdominal ECG recordings, is proposed. In the first stage the maternal R-peaks and fiducial points (QRS onset and offset) are detected, using time-frequency analysis, and the maternal QRS complexes are eliminated. The second stage locates the positions of the candidate fetal R-peaks, using complex wavelets and pattern matching theory techniques. In the third stage, the fetal R-peaks that overlap with the maternal QRS complexes are found. The method is validated using a dataset of 4 long duration recordings and the obtained results indicate high detection ability of the method (96% accuracy).

In this paper we explore the RR-interval duration signal in classifying arrhythmias. Initially the RR-interval duration signal is extracted from the ECGs. Each RR interval is characterized using the MIT-BIH arrhythmia database annotation. We use a 3 RR interval sliding window, centered in the middle RR interval, to classify each beat using a rule-based approach. The rules are obtained from cardiologists and are related to the tachogram morphology in arrhythmic events. Classification is performed for five categories of arrhythmic heats. The classification system is tested using selected beats from the MIT-BIH database (30,000 annotated heats were used). We present results in terms of sensitivity, positive prediction and system's performance. The scores achieved indicate high performance (95.85%) for the classification of the different categories of arrhythmic beats.

Sensor-based quantitative assessment of frailty in older adults usually requires expensive wearables or ambient sensors. However, low cost activity tracking systems can be effective in discriminating frailty characteristics. In this work, a system for assessment of the frailty of older people is presented, based on the analysis of data describing the daily in-house activity. More specifically, room-to-room movements are recorded using a set of Bluetooth beacons, located in fixed positions inside each room. A smartphone is used to locate the timestamps of room transitions, and these are used to calculate the time spent in each room, thus generating the time intervals signal. Then, several features depicting the in-house mobility are extracted from each signal and are evaluated, in terms of correlation with the subject’s frailty status. Furthermore, the features are used to train a classifier for frailty assessment. Two approaches are evaluated, with different number of levels of frailty, and the results indicate that this procedure can lead to reliable older people frailty assessments, especially when two levels of frailty are considered.