Nikolaos S. Katertsidis

Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach

In this paper, we present a methodology and a wearable system for the evaluation of the emotional states of car-racing drivers. The proposed approach performs an assessment of the emotional states using facial electromyograms, electrocardiogram, respiration, and electrodermal activity. The system consists of the following: 1) the multisensorial wearable module; 2) the centralized computing module; and 3) the systems interface. The system has been preliminary validated by using data obtained from ten subjects in simulated racing conditions. The emotional classes identified are high stress, low stress, disappointment, and euphoria. Support vector machines (SVMs) and adaptive neuro-fuzzy inference system (ANFIS) have been used for the classification. The overall classification rates achieved by using tenfold cross validation are 79.3% and 76.7% for the SVM and the ANFIS, respectively.

An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders

Abstract Anxiety disorders are psychiatric disorders characterized by a constant and abnormal anxiety that interferes with daily-life activities. Their high prevalence in the general population and the severe limitations they cause have drawn attention to the development of new and efficient strategies for their treatment. In this work we describe the INTREPID system which provides an innovative and intelligent solution for the monitoring of patients with anxiety disorders during therapeutic sessions. It recognizes an individuals affective state based on 5 pre-defined classes (relaxed, neutral, startled, apprehensive and very apprehensive), from physiological data collected via non-invasive technologies (blood volume pulse, heart rate, galvanic skin response and respiration). The system is validated using data obtained through an emotion elicitation experiment based on the International Affective Picture System. Four different classification algorithms are implemented (Artificial Neural Networks, Support Vector Machines, Random Forests and a Neuro-Fuzzy System). The overall classification accuracy achieved is 84.3%.

A cardiovascular simulator tailored for training and clinical uses

OBJECTIVE In the present work a cardiovascular simulator designed both for clinical and training use is presented. METHOD The core of the simulator is a lumped parameter model of the cardiovascular system provided with several modules for the representation of baroreflex control, blood transfusion, ventricular assist device (VAD) therapy and drug infusion. For the training use, a Pre-Set Disease module permits to select one or more cardiovascular diseases with a different level of severity. For the clinical use a Self-Tuning module was implemented. In this case, the user can insert patients specific data and the simulator will automatically tune its parameters to the desired hemodynamic condition. The simulator can be also interfaced with external systems such as the Specialist Decision Support System (SDSS) devoted to address the choice of the appropriate level of VAD support based on the clinical characteristics of each patient. RESULTS The Pre-Set Disease module permits to reproduce a wide range of pre-set cardiovascular diseases involving heart, systemic and pulmonary circulation. In addition, the user can test different therapies as drug infusion, VAD therapy and volume transfusion. The Self-Tuning module was tested on six different hemodynamic conditions, including a VAD patient condition. In all cases the simulator permitted to reproduce the desired hemodynamic condition with an error<10%. CONCLUSIONS The cardiovascular simulator could be of value in clinical arena. Clinicians and students can utilize the Pre-Set Diseases module for training and to get an overall knowledge of the pathophysiology of common cardiovascular diseases. The Self-Tuning module is prospected as a useful tool to visualize patients status, test different therapies and get more information about specific hemodynamic conditions. In this sense, the simulator, in conjunction with SDSS, constitutes a support to clinical decision - making.

Adverse event prediction in patients with left ventricular assist devices

This work presents the Treatment Tool, which is a component of the Specialists Decision Support Framework (SDSS) of the SensorART platform. The SensorART platform focuses on the management of heart failure (HF) patients, which are treated with implantable, left ventricular assist devices (LVADs). SDSS supports the specialists on various decisions regarding patients with LVADs including decisions on the best treatment strategy, suggestion of the most appropriate candidates for LVAD weaning, configuration of the pump speed settings, while also provides data analysis tools for new knowledge extraction. The Treatment Tool is a web-based component and its functionality includes the calculation of several acknowledged risk scores along with the adverse events appearance prediction for treatment assessment.

INTREPID, a biosignal-based system for the monitoring of patients with anxiety disorders

In this work, we present the concept, the architecture and the evaluation of the INTREPID system. INTREPID is an advanced monitoring system which optimally classifies an individuals emotional state based on 5 pre-defined emotional classes, (relaxed, neutral, startled, apprehensive and very apprehensive), through association of the information arising from specific biological signals (Blood Volume Pulse, Heart Rate, Galvanic Skin Response and Respiration). The system is utilized for the monitoring of patients with anxiety disorder during therapeutic sessions. It is validated using data obtained through an emotion elicitation experiment based on the International Affective Picture System. An overall classification ratio of 85% is obtained.

Fat Droplets Identification in Liver Biopsies using Supervised Learning Techniques

Non-Alcoholic Fatty Liver Disease (NAFLD) is a frequent syndrome that exclusively refers to fat accumulation in liver and steatohepatitis1. It is considered as a massive disease ranging from 20% to 40% in adult populations of the Western World. Its prevalence is related to insulin resistance, which places individuals at high rates of mortality. An increased fat accumulation rate, can significantly increase the development of liver steatosis, which in later stages may progress into fibrosis and cirrhosis. In recent years, research groups focus on the automated fat detection based on histology and digital image processing. The current project, extends our previous work for the detection and quantification of fatty liver, by characterizing histological findings. It is an extensive study of supervised learning of fat droplet features, in order to exclude other findings from fat ratio computation. The method is evaluated on a set of 13 liver biopsy images, performing 92% accuracy.

Designing a gamified social platform for people living with dementia and their live-in family caregivers

In the current paper, a social gamified platform for people living with dementia and their live-in family caregivers, integrating a broader diagnostic approach and interactive interventions is presented. The CAREGIVERSPRO-MMD (C-MMD) platform constitutes a support tool for the patient and the informal caregiver - also referred to as the dyad - that strengthens self-care, and builds community capacity and engagement at the point of care. The platform is implemented to improve social collaboration, adherence to treatment guidelines through gamification, recognition of progress indicators and measures to guide management of patients with dementia, and strategies and tools to improve treatment interventions and medication adherence. Moreover, particular attention was provided on guidelines, considerations and user requirements for the design of a User-Centered Design (UCD) platform. The design of the platform has been based on a deep understanding of users, tasks and contexts in order to improve platform usability, and provide adaptive and intuitive User Interfaces with high accessibility. In this paper, the architecture and services of the C-MMD platform are presented, and specifically the gamification aspects.

Advanced treatment and care for patients receiving Ventricular Assist Device (VAD) therapy through efficient monitoring and intelligent decision support algorithms

The scope of this paper is to present in detail the Specialists main components of the SensorART platform, specifically the Monitoring Application and the Decision Support System (SDSS). The former provides to the specialists tele-monitoring and tele-controlling functionalities, while the latter assists the specialists on deciding the best treatment strategy for a specific patient.