Begoña Garcia-Zapirain

EEG artifact removal—state-of-the-art and guidelines

This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.

Eye/Head Tracking Technology to Improve HCI with iPad Applications

In order to improve human computer interaction (HCI) for people with special needs, this paper presents an alternative form of interaction, which uses the iPads front camera and eye/head tracking technology. With this functional nature/capability operating in the background, the user can control already developed or new applications for the iPad by moving their eyes and/or head. There are many techniques, which are currently used to detect facial features, such as eyes or even the face itself. Open source bookstores exist for such purpose, such as OpenCV, which enable very reliable and accurate detection algorithms to be applied, such as Haar Cascade using very high-level programming. All processing is undertaken in real time, and it is therefore important to pay close attention to the use of limited resources (processing capacity) of devices, such as the iPad. The system was validated in tests involving 22 users of different ages and characteristics (people with dark and light-colored eyes and with/without glasses). These tests are performed to assess user/device interaction and to ascertain whether it works properly. The system obtained an accuracy of between 60% and 100% in the three test exercises taken into consideration. The results showed that the Haar Cascade had a significant effect by detecting faces in 100% of cases, unlike eyes and the pupil where interference (light and shade) evidenced less effectiveness. In addition to ascertaining the effectiveness of the system via these exercises, the demo application has also helped to show that user constraints need not affect the enjoyment and use of a particular type of technology. In short, the results obtained are encouraging and these systems may continue to be developed if extended and updated in the future.

Security Recommendations for mHealth Apps: Elaboration of a Developer's Guide

Being the third fastest-growing app category behind games and utilities, mHealth apps are changing the healthcare model, as medicine today involves the data they compile and analyse, information known as Big Data. However, the majority of apps are lacking in security when gathering and dealing with the information, which becomes a serious problem. This article presents a guide regarding security solution, intended to be of great use for developers of mHealth apps. In August 2015 current mobile health apps were sought out in virtual stores such as Android Google Play, Apple iTunes App Store etc., in order to classify them in terms of usefulness. After this search, the most widespread weaknesses in the field of security in the development of these mobile apps were examined, based on sources such as the “OWASP Mobile Security Project, the initiative recently launched by the Office of Civil Rights (OCR), and other articles of scientific interest. An informative, elemental guide has been created for the development of mHealth apps. It includes information about elements of security and its implementation on different levels for all types of mobile health apps based on the data that each app manipulates, the associated calculated risk as a result of the likelihood of occurrence and the threat level resulting from its vulnerabilities - high level (apps for monitoring, diagnosis, treatment and care) from 6 ≤ 9, medium level (calculator, localizer and alarm) from 3 ≤ 6 and low level (informative and educational apps) from 0 ≤ 3. The guide aims to guarantee and facilitate security measures in the development of mobile health applications by programmers unconnected to the ITC and professional health areas.

Monitoring and Follow-up of Chronic Heart Failure: a Literature Review of eHealth Applications and Systems

In developed countries heart failure is one of the most important causes of death, followed closely by strokes and other cerebrovascular diseases. It is one of the major healthcare issues in terms of increasing number of patients, rate of hospitalizations and costs. The main aim of this paper is to present telemedicine applications for monitoring and follow-up of heart failure and to show how these systems can help reduce costs of administering heart failure. The search for e-health applications and systems in the field of telemonitoring of heart failure was pursued in IEEE Xplore, Science Direct, PubMed and Scopus systems between 2005 and the present time. This search was conducted between May and June 2015, and the articles deemed to be of most interest about treatment, prevention, self-empowerment and stabilization of patients were selected. Over 100 articles about telemonitoring of heart failure have been found in the literature reviewed since 2005, although the most interesting ones have been selected from the scientific standpoint. Many of them show that telemonitoring of patients with a high risk of heart failure is a measure that might help to reduce the risk of suffering from the disease. Following the review conducted, in can be stated that via the research articles analysed that telemonitoring systems can help to reduce the costs of administering heart failure and result in less re-hospitalization of patients.

A New mHealth App for Monitoring and Awareness of Healthy Eating: Development and User Evaluation by Spanish Users

Modern-day society has moved towards a more sedentary lifestyle. Advances in technology and changes in habits in our daily lives have led a large part of the population towards a spiralling sedentary lifestyle and obesity. The main objective of this work is to develop and subsequently assess a mobile app, named DietApp, that provides advice about obtaining a healthy diet according to age, clinical history and physical condition. DietApp has been developed for iOS and Android systems, and a survey comprising 7 simple questions enabled the app to be evaluated on a user level by taking into account aspects such as its usefulness and ease of use. DietApp was assessed by 150 Spanish individuals between 18 and 69 years of age, and 84% of them thought it was easy to use. 80% of users also considered the dietary suggestions provided by the app to be very useful while 62% were of the opinion that it is very useful in general. All of them would recommend the app to other users. During the six months when the app was used, any dietary excess or shortcomings were corrected in 72% of those interviewed. A mobile app has been created that is easy to use and attractive, providing personalised suggestions according to illness that are useful for the individual.

Sphero as an interactive tool in computer games for people with ID

This paper presents an experiment in which people with intellectual disabilities played 3 apps with different levels and purposes on an iPad Air. As part of the game they were required to interact with Sphero and the activities they tried to perform required motor skills, attention and memory. We have selected two groups of participants according to their level of support needs. The results show the highest level of satisfaction in both groups in the game involving Sphero. In terms of effectiveness of activities for this profile of participant as interventions the results reveal that driving and care activities do show an adequate response, though with worse results related to memory.

Serious games to promote independent living for intellectually disabled people: Starting with shopping

The project described in this article consists of the design and implementation of a serious games application to promote independent living for adolescents/adults with intellectual disability (ID). We present this first stage of a broader project which aims to teach this group how to shop. Given the wide acceptance of new technologies among people with ID, iPads were used as a support tool. The application is based on a set of games with different features and aims and includes advice for shopping. The design includes real situations which focus on making the application useful and attractive, thus motivating users. The results obtained after the tests carried out with real users were positive and encouraging.

Classification of pressure ulcer tissues with 3D convolutional neural network

AbstractA 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising. Graphical AbstractThe Classification of Pressure Ulcer Tissues Based on 3D Convolutional Neural Network.

Comparison of background EEG activity of different groups of patients with idiopathic epilepsy using Shannon spectral entropy and cluster-based permutation statistical testing

Idiopathic epilepsy is characterized by generalized seizures with no apparent cause. One of its main problems is the lack of biomarkers to monitor the evolution of patients. The only tools they can use are limited to inspecting the amount of seizures during previous periods of time and assessing the existence of interictal discharges. As a result, there is a need for improving the tools to assist the diagnosis and follow up of these patients. The goal of the present study is to compare and find a way to differentiate between two groups of patients suffering from idiopathic epilepsy, one group that could be followed-up by means of specific electroencephalographic (EEG) signatures (intercritical activity present), and another one that could not due to the absence of these markers. To do that, we analyzed the background EEG activity of each in the absence of seizures and epileptic intercritical activity. We used the Shannon spectral entropy (SSE) as a metric to discriminate between the two groups and performed permutation-based statistical tests to detect the set of frequencies that show significant differences. By constraining the spectral entropy estimation to the [6.25–12.89) Hz range, we detect statistical differences (at below 0.05 alpha-level) between both types of epileptic patients at all available recording channels. Interestingly, entropy values follow a trend that is inversely related to the elapsed time from the last seizure. Indeed, this trend shows asymptotical convergence to the SSE values measured in a group of healthy subjects, which present SSE values lower than any of the two groups of patients. All these results suggest that the SSE, measured in a specific range of frequencies, could serve to follow up the evolution of patients suffering from idiopathic epilepsy. Future studies remain to be conducted in order to assess the predictive value of this approach for the anticipation of seizures.

A First Summarization System of a Video in a Target Language

In this paper, we present the first results of the project AMIS (Access Multilingual Information opinionS) funded by Chist-Era. The main goal of this project is to understand the content of a video in a foreign language. In this work, we consider the understanding process, such as the aptitude to capture the most important ideas contained in a media expressed in a foreign language. In other words, the understanding will be approached by the global meaning of the content of a support and not by the meaning of each fragment of a video.