Contributions to physiological computing by means of automatic learning.

169 p.El trabajo presentado en esta tesis se enmarca dentro de dos áreas dentro de la computación fisiológica, que a su vez forma parte de las ciencias de la computación. La primera área trabajada corresponde a la de la detección de fenómenos psicológicos y estados mentales mediante la monitorización de las variables fisiológicas de las personas. La segunda área que se estudia en esta tesis forma parte del estudio de formas alternativas de interacción: los interfaces cerebro-computador.La primera contribución mejora un sistema de lógica difusa que, mediante la monitorización de las señales fisiológicas, es capaz de dar una estimación continuada en el tiempo del nivel del estrés mental. La segunda contribución continua con esta línea y estudia la detección de las respuestas fisiológicas del fenómeno opuesto al estrés: la relajación. En esta contribución se presentan características innovadoras que facilitan dicha detección y la pone en práctica con métodos de aprendizaje automático.Finalmente, la tercera contribución estudia diferentes técnicas de aprendizaje para distinguir entre cuatro clases de movimiento más una quinta clase de no intencionalidad de movimiento en un problema de BCI

Automatic detection of the mental state in responses towards relaxation

Nowadays, considering society’s highly demanding lifestyles, it is important to consider the usefulness of relaxation from the perspective of both psychology and clinical practice. The response towards relaxation (RResp) is a mind-body interaction that relaxes the organism or compensates for the physiological effects caused by stress. This work aims to automatically detect the different mental states (relaxation, rest and stress) in which RResps may occur so that complete feedback about the quality of the relaxation can be given to the subject itself, the psychologist or the doctor. To this end, an experiment was conducted to induce both states of stress and relaxation in a sample of 20 university students (average age of 25.76±3.7 years old). The electrocardiographic and electrodermal activity signals collected from the participants produced a dataset with 1641 episodes or instances in which the previously mentioned mental states take place. This data was used to extract up to 50 features and train several supervised learning algorithms (rule-based, trees, probabilistic, ensemble classifiers, etc.) using and not using feature selection techniques. Besides, the authors synthesised the cardiac activity information into a single new feature and discretised it down to three levels. The experimentation revealed which features were most discriminating, reaching a classification average accuracy of up to 94.01±1.73% with the 6 most relevant features for the own-collected dataset. Finally, being restrictive, the same solution/subspace was tested with a dataset referenced in the bibliography (WESAD) and scored an average accuracy of 90.36±1.62%.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially funded by the Department of Education, Universities and Research of the Basque Government (ADIAN, IT-980-16); and by the Spanish Ministry of Science, Innovation and Universities—National Research Agency and the European Regional Development Fund—ERDF (PhysComp, TIN2017-85409-P), and from the State Research Agency (AEI, Spain) under Grant Agreement No RED2018-102312-T (IA-Biomed)

A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface

A brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to send some message to a certain device. This study presents a proposal, based on a hierarchical structured system, for recognising intentional and non-intentional mental tasks on a BCI system by applying machine learning techniques to the EEG signals. First-level clustering is performed to distinguish between intentional control (IC) and non-intentional control (NC) state patterns. Then, the patterns recognised as IC are passed on to a second stage where supervised learning techniques are used to classify them. In BCI applications, it is critical to correctly classify NC states with a low false positive rate (FPR) to avoid undesirable effects. According to the literature, we selected a maximum FPR of 10%. Under these conditions, our proposal achieved an average test accuracy of 66.6%, with an 8.2% FPR, for the BCI competition IIIa dataset. The main contribution of this paper is the hierarchical approach, based on machine learning paradigms, which performs intentional and non-intentional discrimination and, depending on the case, classifies the intended command selected by the user.This work was partially supported by the ERDF/Spanish Ministry of Science, Innovation and Universities - National Research Agency/PhysComp project, TIN2017-85409-P and by the Department of Education, Universities and Research of the Basque Government (ADIAN research group, grant IT980-16)

A Hierarchical Machine Learning Solution for the Non-Invasive Diagnostic of Autonomic Dysreflexia

More than half of patients with high spinal cord injury (SCI) suffer from episodes of autonomic dysreflexia (AD), a condition that can lead to lethal situations, such as cerebral haemorrhage, if not treated correctly. Clinicians assess AD using clinical variables obtained from the patient’s history and physiological variables obtained invasively and non-invasively. This work aims to design a machine learning-based system to assist in the initial diagnosis of AD. For this purpose, 29 patients with SCI participated in a test at Cruces University Hospital in which data were collected using both invasive and non-invasive methods. The system proposed in this article is based on a two-level hierarchical classification to diagnose AD and only uses 35 features extracted from the non-invasive stages of the experiment (clinical and physiological features). The system achieved a 93.10% accuracy with a zero false negative rate for the class of having the disease, an essential condition for treating patients according to medical criteria.This work was partially funded by the Department of Education, Universities and Research of the Basque Government (ADIAN, IT-980-16), by the Spanish Ministry of Science, Innovation and Universities-National Research Agency and the European Regional Development Fund-ERDF (PhysComp, TIN2017-85409-P), and from the State Research Agency (AEI, Spain) under grant agreement No. RED2018-102312-T (IA-Biomed)

A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm

Goal: This paper proposes a reliable stress and relaxation level estimation algorithm that is implemented in a portable, low-cost hardware device and executed in real time. The main objective of this work is to offer an affordable and & x201C;ready-to-go & x201D; solution for medical and personal environments, in which the detection of the arousal level of a person is crucial. Methods: To achieve meaningful identification of stress and relaxation, a fuzzy algorithm based on expert knowledge is built according to parameters extracted from physiological records. In addition to the heart rate, parameters extracted from the galvanic skin response and breath are employed to extend the results. Moreover, this algorithm achieves accurate results with a restricted computational load and can be implemented in a miniaturized low-cost prototype. The developed solution includes standard and actively shielded electrodes that are connected to an Arduino device for acquisition, while parameter extraction and fuzzy processing are conducted with a more powerful Raspberry Pi board. The proposed solution is validated using real physiological registers from 42 subjects collected using BIOPAC MP36 hardware. Additionally, a real-time acquisition, processing and remote cloud storage service is integrated via IoT wireless technology. Results: Robust identification of stress and relaxation is achieved, with F1 scores of 91.15 & x0025; and 96.61 & x0025;, respectively. Moreover, processing is performed using a 20-second sliding window; thus, only a small frame of context is required. Significance: This work presents a reliable solution for identifying stress and relaxation levels in real time, which can lead to the production of low-cost commercial devices for use in medical and personal environments.This work was supported in part by the ERDF/Spanish Ministry of Science, Innovation and Universities, National Research Agency/PhysComp Project under Grant TIN2017-85409-P