Modeling andsimulationofspeedselectiononleftventricular assist devices

The control problem for LVADs is to set pump speed such that cardiac output and pressure perfusion are within acceptable physiological ranges. However, current technology of LVADs cannot provide for a closed-loop control scheme that can make adjustments based on the patient\u27s level of activity. In this context, the SensorART Speed Selection Module (SSM) integrates various hardware and software components in order to improve the quality of the patients\u27 treatment and the workflow of the specialists. It enables specialists to better understand the patient-device interactions, and improve their knowledge. The SensorART SSM includes two tools of the Specialist Decision Support System (SDSS); namely the Suction Detection Tool and the Speed Selection Tool. A VAD Heart Simulation Platform (VHSP) is also part of the system. The VHSP enables specialists to simulate the behavior of a patient?s circulatory system, using different LVAD types and functional parameters. The SDSS is a web-based application that offers specialists with a plethora of tools for monitoring, designing the best therapy plan, analyzing data, extracting new knowledge and making informative decisions. In this paper, two of these tools, the Suction Detection Tool and Speed Selection Tool are presented. The former allows the analysis of the simulations sessions from the VHSP and the identification of issues related to suction phenomenon with high accuracy 93%. The latter provides the specialists with a powerful support in their attempt to effectively plan the treatment strategy. It allows them to draw conclusions about the most appropriate pump speed settings. Preliminary assessments connecting the Suction Detection Tool to the VHSP are presented in this paper

Spectral information of EEG signals with respect to epilepsy classification

Abstract Background The spectral information of the EEG signal with respect to epilepsy is examined in this study. Method In order to assess the impact of the alternative definitions of the frequency sub-bands that are analysed, a number of spectral thresholds are defined and the respective frequency sub-band combinations are generated. For each of these frequency sub-band combination, the EEG signal is analysed and a vector of spectral characteristics is defined. Based on this feature vector, a classification schema is used to measure the appropriateness of the specific frequency sub-band combination, in terms of epileptic EEG classification accuracy. Results The obtained results indicate that additional frequency band analysis is beneficial towards epilepsy detection. Conclusions This work includes the first systematic assessment of the impact of the frequency sub-bands to the epileptic EEG classification accuracy, and the obtained results revealed several frequency sub-band combinations that achieve high classification accuracy and have never been reported in the literature before

Intelligent methods for cardiovascular diseases diagnosis

In this thesis a generic methodology for the automated generation of fuzzy models is presented. The methodology includes three stages: (i) crisp model extraction, (ii) fuzzy model creation, and (iii) parameter optimization. In the first stage, a crisp model is created; depending on the approach, which will be employed for the crisp model creation, the methodology can be knowledge-based, if the initial crisp model is defined by experts, or data-driven, if the initial crisp model is mined from the available data. In the second stage, this crisp model is transformed to the corresponding fuzzy model; several new parameters are introduced due to the fuzzification of the decision boundaries. Finally (in the third stage), optimization is performed in order to optimally define all the parameters entering the fuzzy model. The methodology is generic, thus different realizations can be created. The realizations created are the crisp approach, which consists only from the first stage of the methodology, the knowledge based approach, where the initial crisp set of rules is defined by experts, and the data-driven approach, where data mining techniques are employed in order to generate the initial crisp set of rules directly from the data. In some cases weights are included in the fuzzy model. The methodology is applied to the cardiovascular diseases domain. More specifically, the methodology is employed in order to automatically generate fuzzy models that deal with the problem of arrhythmic beat classification in ECG, the ischemic beat classification in ECG and the coronary artery disease diagnosis. In all cases, the fuzzy models present comparable or higher accuracy than other approaches presented in the literature. In addition, a large number of benchmark classification datasets is used for the evaluation and the reported results indicate high classification accuracy. The methodology presents several advantages and novelties: it is generic since it is not based on a specific technique for crisp model generation; expert knowledge or any rule-mining technique can be adapted to generate the crisp model. This offers flexibility, since the methodology does not depend on the domain of application. This is advantageous since it can integrate state-of-the-art rule mining methods, as well as future developments or even hybrid approaches, combining expert knowledge with the mined knowledge. Also, different approaches concerning the elements of the fuzzy model and alternative optimization techniques can be integrated. Another major advantage of the methodology is that the transformation of the crisp model to the respective fuzzy one, is straightforward, ensuring in this way the fully automated nature of the methodology. In the case of the data-driven approach, the generation of fuzzy models, based on the fuzzification and optimization of a set of rules extracted from the decision tree, instead of a fuzzy decision tree (widely proposed in the literature), is a novel feature which offers more flexibility and adaptation ability to a specific dataset, while keeping the complexity of the decision making process the same. Finally, the introduction of class weights is a novel feature which allows the fuzzy model to be more flexible and adaptable.Στην παρούσα διδακτορική διατριβή παρουσιάζεται γενική μεθοδολογία για την αυτόματη παραγωγή ασαφών μοντέλων λήψης απόφασης, η οποία αποτελείται από τρία στάδια. Αρχικά, ορίζεται ένα σύνολο σαφών κανόνων που αποτελεί το σαφές μοντέλο. Οι κανόνες μπορούν να προέρχονται είτε από ειδικούς του τομέα εφαρμογής ή εξάγονται απευθείας από τα δεδομένα με χρήση μεθόδων εξόρυξης δεδομένων. Στην συνέχεια, οι σαφείς κανόνες μετατρέπονται σε ασαφείς, με αποτέλεσμα τον ορισμό ενός ασαφούς μοντέλου. Τέλος, όλες οι τιμές των παραμέτρων που χρησιμοποιούνται στο ασαφές μοντέλο ορίζονται με μια διαδικασία βελτιστοποίησης, με αποτέλεσμα την παραγωγή του τελικού ασαφούς μοντέλου. Η μεθοδολογία είναι γενική και μπορεί να υλοποιηθεί με πολλούς διαφορετικούς τρόπους. Οι προσεγγίσεις που επιχειρήθηκαν είναι η γνωσιακή σαφής προσέγγιση, που αποτελεί προοίμιο της γενικής μεθοδολογίας καθώς περιλαμβάνει μόνο το πρώτο από τα στάδιά της, η γνωσιακή ασαφής προσέγγιση, στην οποία η αρχική γνώση προέρχεται από ειδικούς του τομέα εφαρμογής και η οδηγούμενη-από-τα-δεδομένα ασαφής προσέγγιση, στην οποία η αρχική γνώση εξάγεται από τα δεδομένα. Σε κάποιες από αυτές χρησιμοποιήθηκαν βάρη στο τελικό ασαφές μοντέλο. Η μεθοδολογία εφαρμόστηκε σε προβλήματα αυτόματης διάγνωσης καρδιαγγειακών παθήσεων και πιο συγκεκριμένα, την ταξινόμηση αρρυθμικών καρδιακών παλμών, την ταξινόμηση ισχαιμικών καρδιακών παλμών και την διάγνωση της στεφανιαίας νόσου. Επίσης, η μεθοδολογία εφαρμόστηκε σε γνωστά προβλήματα ταξινόμησης, για να είναι δυνατή η σύγκριση με υπάρχουσες προσεγγίσεις. Τόσο η ίδια η μεθοδολογία όσο και οι υλοποιήσεις που προέκυψαν από αυτή παρουσιάζουν πλεονεκτήματα σε σχέση με παρόμοιες προσεγγίσεις που έχουν παρουσιαστεί στην υπάρχουσα βιβλιογραφία. Η γνωσιακή ασαφής υλοποίηση αποτελεί πρωτότυπη προσέγγιση ενώ η οδηγούμενη-από-τα-δεδομένα ασαφής υλοποίηση με χρήση δέντρων απόφασης εισάγει την καινοτομία της μετατροπής του ασαφούς δέντρου σε σύνολο κανόνων. Και στις δύο περιπτώσεις, η χρήση βαρών κατηγορίας αποτελεί πρωτότυπο μεθοδολογικό στοιχείο ενώ η δυνατότητα τεκμηρίωσης των αποφάσεων που παράγονται είναι βασικό πλεονέκτημα. Η εφαρμογή σε συγκεκριμένα ιατρικά πεδία είναι επίσης πρωτότυπη, ενώ η συνολική αξιολόγηση με γνωστά προβλήματα ταξινόμησης αναδεικνύει την δυνατότητα της μεθοδολογίας να παράγει αυτόματα ασαφή μοντέλα που παρουσιάζουν υψηλή επίδοση

Personalized UV Radiation Risk Monitoring Using Wearable Devices and Fuzzy Modeling

This paper presents a solution for monitoring of solar ultraviolet (UV) exposure and alerting about risks in real time. The novel system provides smart personalized indications for solar radiation protection. The system consists of a sensing device and a mobile application. The sensing device monitors solar radiation in real time and transmits the values wirelessly to a smart device, in which the mobile application is installed. Then, the mobile application processes the values from the sensory apparatus, based on a fuzzy expert system (FES) created from personal information (hair and eye color, tanning and burning frequency), which are entered by the user answering a short questionnaire. The FES provides an estimation of the recommended time of safe exposure in direct sunlight. The proposed system is designed to be portable (a wearable sensing device and smartphone) and low cost, while supporting multiple users

Cognitive assessment based on electroencephalography analysis in Virtual and Augmented Reality environments, using Head Mounted Displays: A systematic review

The objective of this systematic review is to investigate cognitive assessment based on electroencephalography (EEG) analysis in Virtual Reality (VR) and Augmented Reality (AR) environments, projected on Head Mounted Displays (HMD), on healthy individuals

Evaluation of the User Adaptation in a BCI Game Environment

Brain-computer interface (BCI) technology is a developing field of study with numerous applications. The purpose of this paper is to discuss the use of brain signals as a direct communication pathway to an external device. In this work, Zombie Jumper is developed, which consists of 2 brain commands, imagining moving forward and blinking. The goal of the game is to jump over static or moving “zombie” characters in order to complete the level. To record the raw EEG data, a Muse 2 headband is used, and the OpenViBE platform is employed to process and classify the brain signals. The Unity engine is used to build the game, and the lab streaming layer (LSL) protocol is the connective link between Muse 2, OpenViBE and the Unity engine for this BCI-controlled game. A total of 37 subjects tested the game and played it at least 20 times. The average classification accuracy was 98.74%, ranging from 97.06% to 99.72%. Finally, playing the game for longer periods of time resulted in greater control

A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults

Indoor localization systems have already wide applications mainly for providing localized information and directions. The majority of them focus on commercial applications providing information such us advertisements, guidance and asset tracking. Medical oriented localization systems are uncommon. Given the fact that an individual’s indoor movements can be indicative of his/her clinical status, in this paper we present a low-cost indoor localization system with room-level accuracy used to assess the frailty of older people. We focused on designing a system with easy installation and low cost to be used by non technical staff. The system was installed in older people houses in order to collect data about their indoor localization habits. The collected data were examined in combination with their frailty status, showing a correlation between them. The indoor localization system is based on the processing of Received Signal Strength Indicator (RSSI) measurements by a tracking device, from Bluetooth Beacons, using a fingerprint-based procedure. The system has been tested in realistic settings achieving accuracy above 93% in room estimation. The proposed system was used in 271 houses collecting data for 1–7-day sessions. The evaluation of the collected data using ten-fold cross-validation showed an accuracy of 83% in the classification of a monitored person regarding his/her frailty status (Frail, Pre-frail, Non-frail)