Ring-Topology Echo State Networks for ICU Sepsis Classification

Sepsis is a life threatening condition that can be treated if detected early. This paper presents a study of the application of a Ring Topology Echo State Network (ESN) algorithm to a sepsis prediction task based on ICU records. The implemented algorithm is compared with commonly used classifiers and a combination of both approaches. Finally, we address how different causal strategies on filling missing record values affected the final classification performances. Having a dataset with a limited number of time entries per patient, the utility score U = 0.188 obtained (team 51: PLUX) suggests that further research is needed in order for the ESN to capture the temporal dynamics of the problem at hand

Experiencing discomfort: designing for affect from first-person perspective

In this paper, we describe how by embracing a first-person design perspective we engaged with the uncomfortable to successfully gain insight into the design of affective technologies. Firstly, we experience estrangement that highlights and grounds our bodies as desired in the targeted technology interaction. Secondly, we understand design preconceptions, risks and limitations of the design artifacts

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism. Fatigue can be seen as a subjective or objective phenomenon. Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon. Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline. )erefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries. In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements. Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier. )e system, based on 9 main combined features, achieves fatigue regime classification performances of 0.82 ± 0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached. Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state

Biosensing and Actuation—Platforms Coupling Body Input-Output Modalities for Affective Technologies

Research in the use of ubiquitous technologies, tracking systems and wearables within mental health domains is on the rise. In recent years, affective technologies have gained traction and garnered the interest of interdisciplinary fields as the research on such technologies matured. However, while the role of movement and bodily experience to affective experience is well-established, how to best address movement and engagement beyond measuring cues and signals in technology-driven interactions has been unclear. In a joint industry-academia effort, we aim to remodel how affective technologies can help address body and emotional self-awareness. We present an overview of biosignals that have become standard in low-cost physiological monitoring and show how these can be matched with methods and engagements used by interaction designers skilled in designing for bodily engagement and aesthetic experiences. Taking both strands of work together offers unprecedented design opportunities that inspire further research. Through first-person soma design, an approach that draws upon the designer’s felt experience and puts the sentient body at the forefront, we outline a comprehensive work for the creation of novel interactions in the form of couplings that combine biosensing and body feedback modalities of relevance to affective health. These couplings lie within the creation of design toolkits that have the potential to render rich embodied interactions to the designer/user. As a result we introduce the concept of “orchestration”. By orchestration, we refer to the design of the overall interaction: coupling sensors to actuation of relevance to the affective experience; initiating and closing the interaction; habituating; helping improve on the users’ body awareness and engagement with emotional experiences; soothing, calming, or energising, depending on the affective health condition and the intentions of the designer. Through the creation of a range of prototypes and couplings we elicited requirements on broader orchestration mechanisms. First-person soma design lets researchers look afresh at biosignals that, when experienced through the body, are called to reshape affective technologies with novel ways to interpret biodata, feel it, understand it and reflect upon our bodies

Unpacking Non-Dualistic Design: The Soma Design Case

We report on a somaesthetic design workshop and the subsequent analytical work aiming to demystify what is entailed in a non-dualistic design stance on embodied interaction and why a first-person engagement is crucial to its unfoldings. However, as we will uncover through a detailed account of our process, these first-person engagements are deeply entangled with second- and third-person perspectives, sometimes even overlapping. The analysis furthermore reveals some strategies for bridging the body-mind divide by attending to our inner universe and dissolving or traversing dichotomies between inside and outside; individual and social; body and technology. By detailing the creative process, we show how soma design becomes a process of designing with and through kinesthetic experience, in turn letting us confront several dualisms that run like fault lines through HCI's engagement with embodied interaction

Making Biosignals Available

This thesis is an invitation to rethink how simplistic approaches to biosignal processing enrich the potential of personal sensing in line with the fast development of what have come to be undeniably ubiquitous technologies. Moreover, the aim of taking signal processing, biomedical engineering techniques and machine learning, “Making biosensing available” to design with, is at the core of the thesis. Entering interaction design, pushing for mid tools that expose the affordances of the technologies used, embracing first-person and body-centred design and addressing how biosignal data touches sense-making, ownership and interpretation, a disruption that makes society reflect on how sociodigital materials affect our lives is sought.Esta tesis es una invitación a repensar cómo ciertos abordajes simplistas en procesamiento de bioseñales enriquecen el potencial de los sensores personales, en línea con el rápido desarrollo de lo que han acabado por ser tecnologías innegablemente ubicuas. Más allá de ello, el objetivo de combinar el procesamiento de señales, las técnicas de la ingeniería biomédica y aprendizaje automático, para hacer que los biosensores estén disponibles” para diseñar con ellos, constituye el núcleo central de esta tesis. Abordando el diseño de interacciones, apostando por herramientas intermedias que expongan las potencialidades de las tecnologías utilizadas, abrazando el diseño en primera-persona y centrado en el cuerpo, y analizando cómo los datos de bioseñales tienen un impacto en la construcción de significado, su posesión e interpretación, se busca una disrupción que haga a nuestra sociedad reflexionar sobre cómo los materiales sociodigitales afectan a nuestras vidas.Programa de Doctorat en Informàtic

A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection

We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection approaches.This work was partially funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and Fondo Europeo de Desarrollo Regional (FEDER) and the European Social Fund through project TEC2016-80063-C3-3-R (MINECO/AEI/FEDER/UE). MA was supported by the Beca de colaboración 012/2016 UIB fellowship on Information processing in neural and photonic systems. MS was supported by the Spanish Ministerio de Economía, Industria y Competitividad through a Ramón y Cajal Fellowship (RYC-2015-18140). SO was supported by the Conselleria d'Innovació, Recerca i Turisme del Govern de les Illes Balears and the European Social Fund.Peer reviewe

Exploring Awareness of Breathing through Deep Touch Pressure

Deep Pressure Therapy relies on exerting firm touch to help individuals with sensory sensitivity. We performed first-person explorations of deep pressure enabled by shape-changing actuation driven by breathing sensing. This revealed a novel design space with rich, evocative, aesthetically interesting interactions that can help increase breathing awareness and appreciation through: (1) applying symmetrical as well as asymmetrical pressure on the torso; (2) using pressure to direct attention to muscles or bone structure involved in different breathing patterns; (3) apply synchronous as well as asynchronous feedback following or opposing the user’s breathing rhythm through applying rhythmic pressure. Taken together these explorations led us to design (4) breathing correspondence interactions – a balance point right between leading and following users’ breathing patterns by first applying deep pressure – almost to the point of being unpleasant – and then releasing in rhythmic flow.QC 20210521</p

Automated real-time method for ventricular heartbeat classification

[Background and objective] In this work, we develop a fully automatic and real-time ventricular heartbeat classifier based on a single ECG lead. Single ECG lead classifiers can be especially useful for wearable technologies that provide continuous and long-term monitoring of the electrocardiogram. These wearables usually have a few non-standard leads and the quality of the signals depends on the user physical activity.[Methods] The proposed method uses an Echo State Network (ESN) to classify ECG signals following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations with an inter-patient scheme. To achieve real-time classification, the classifier itself and the feature extraction approach are fast and computationally efficient. In addition, our approach allows transferring the knowledge from one database to another without additional training.[Results] The classification performance of the proposed model is validated on the MIT-BIH arrhythmia and INCART databases. The sensitivity and precision of the proposed method for MIT-BIH arrhythmia database are 95.3 and 88.8 for the modified lead II and 90.9 and 89.2 for the V1 lead. The results reported are further compared to the existing methodologies in literature. Our methodology is a competitive single lead ventricular heartbeat classifier, that is comparable to state-of-the-art algorithms using multiple leads.[Conclusions] The proposed fully automated, single-lead and real-time heartbeat classifier of ventricular heartbeats reports an improved classification accuracy in different leads of the evaluated databases in comparison with other single lead heartbeat classifiers. These results open the possibility of applying our methodology to wearable long-term monitoring devices with an unconventional placement of the electrodes.This work is partially supported by the Spanish Ministerio de Economa y Competitividad (MINECO) and Fondo Europeo de Desarrollo Regional (FEDER) through project TEC2016-80063-C3-3-R. Silvia Ortín was supported by the Conselleria d’Innovació, Recerca i Turisme del Govern de les Illes Balears and the European Social Fund. Miguel Cornelles Soriano was supported by the Spanish Ministerio de Economa, Industria y Competitividad through a Ramon y Cajal Fellowship (RYC-2015-18140).Peer reviewe

Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

International audienceAtrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate data analysis. Many of these automatic techniques have been proposed over the last years. This work presents an overview of research studies of AF detection based on RR interval time series. The aim of this paper is to provide the scientific community and newcomers to the field of AF screening with a resource that presents introductory concepts, clinical features, and a literature review that describes the techniques that are mostly followed when RR interval time series are used for accurate detection of AF