Identification of the stress and relaxation level in people, based on the study and the advanced processing of physiological signals related to the activity of the autonomic nervous system

177 p.The objective of this thesis is the development and implementation of intelligent algorithms for the real-time processing of non-invasively acquired physiological signals to automatically predict the continuous level of stress and relaxation in people. Thus, be able to identify the activity associated with the autonomic nervous system, responsible for the alterations caused in the homeostatic balance within the body. This goal resulted in a solution that goes from the analysis and processing of physiological signals to the design of an algorithm for real-time prediction of the level of stress and relaxation, which has subsequently been implemented in a functional low-cost hardware prototype. More precisely, the physiological records used to carry out this development are the electrocardiogram, the galvanic skin response and breathing due to their relation with the activity of the autonomic nervous system and the possibility of being acquired non-invasively.The proposed methodology focuses on four main aspects. The first is the processing of physiological signals in short-term sliding windows, which contributes to improve the techniques used for the extraction of heart period through the design of novel algorithms focused on the robust analysis of theelectrocardiogram and blood pressure signals. In the second, the analysis, normalization and labeling of the extracted physiological parameters is carried out using original and validated methodologies. In the third aspect, the resulting data are subsequently employed for the design and training of intelligent systems through the implementation of supervised and unsupervised learning techniques in order to carry out a robust prediction of the level of stress and relaxation. Among the validated methods fuzzy logic, fuzzy rule-based supervised learning systems and artificial neural networks stand out. Finally, the development is successfully implemented in a portable low-cost hardware solution consisting of a physiological signal acquisition module and a server that processes and transfers the information to the client safely in real-time

Identification of the stress and relaxation level in people, based on the study and the advanced processing of physiological signals related to the activity of the autonomic nervous system

177 p.The objective of this thesis is the development and implementation of intelligent algorithms for the real-time processing of non-invasively acquired physiological signals to automatically predict the continuous level of stress and relaxation in people. Thus, be able to identify the activity associated with the autonomic nervous system, responsible for the alterations caused in the homeostatic balance within the body. This goal resulted in a solution that goes from the analysis and processing of physiological signals to the design of an algorithm for real-time prediction of the level of stress and relaxation, which has subsequently been implemented in a functional low-cost hardware prototype. More precisely, the physiological records used to carry out this development are the electrocardiogram, the galvanic skin response and breathing due to their relation with the activity of the autonomic nervous system and the possibility of being acquired non-invasively.The proposed methodology focuses on four main aspects. The first is the processing of physiological signals in short-term sliding windows, which contributes to improve the techniques used for the extraction of heart period through the design of novel algorithms focused on the robust analysis of theelectrocardiogram and blood pressure signals. In the second, the analysis, normalization and labeling of the extracted physiological parameters is carried out using original and validated methodologies. In the third aspect, the resulting data are subsequently employed for the design and training of intelligent systems through the implementation of supervised and unsupervised learning techniques in order to carry out a robust prediction of the level of stress and relaxation. Among the validated methods fuzzy logic, fuzzy rule-based supervised learning systems and artificial neural networks stand out. Finally, the development is successfully implemented in a portable low-cost hardware solution consisting of a physiological signal acquisition module and a server that processes and transfers the information to the client safely in real-time

Online robust R-peaks detection in noisy electrocardiograms using a novel iterative smart processing algorithm

Nowadays, many contributions deal with R-peak detection in Electrocardiographic (ECG) signals. Although they present an accurate performance in detection, most of these are presented as offline solutions, both to be processed in high performance platforms (un- der a big cost), or to be analyzed in laboratories without constraints in time, neither in computational load. Owing to this, it is also very important to take one step further, try- ing to develop new solutions which work in portable/wearable low-cost platforms, with constraints in time and in computational load. In this work, an accurate and computationally efficient method for online and robust detection of R-Peaks is presented. This method is divided in three main stages: first, in the pre-processing stage, a complete elimination of artifacts is performed based on a noise and signal intensity approach; second, R-peaks detection is carried out through an effi- cient “area over the curve”method; finally, in the third stage, a novel iterative algorithm consisting in three sequential state machines performs the correct detection of the R-peaks applying heart period distance rules. Moreover, the method is performed over time in short length sliding windows. The algorithm has been tested using all 48 full-length ECG records of the MIT-BIH Ar- rhythmia Database, achieving 99.54% sensitivity and 99.60% positive predictivity in R-peak detection.This work has been performed thanks to the support of the University of the Basque Country (UPV/EHU), the In- telligent Control Research Group of the UPV/EHU, the Pacific Atlantic Network for Technical Higher Education and Re- search (PANTHER) program and the Institute of Biomedical Technologies (IBTec) of the Auckland University of Technology https://doi.org/10.13039/10 0 0 08205, to which the authors are very gratefu

An enhanced fuzzy algorithm based on advanced signal processing for identification of stress

Nowadays, it is crucial to promote and develop the autonomy of people, and specifically of individuals with some disability, in order to improve their life quality and achieve a better inclusion into socio-cultural life. Therefore, the identification of stress situations can be a suitable assistive tool for improving their socio-cultural inclusion. This work presents important enhancements and variations for an existing fuzzy logic stress detection system based on monitoring and processing different physiological signals (heart rate, galvanic skin response and breath). First, it proposes a method based on wavelet processing to improve the detection of R peaks of electrocardiograms. Afterwards, it proposes to decompose the galvanic response signal into two components: the average value and the variations. In addition, it proposes to extract information out the breath signal by analyzing its frequential composition. Finally, an improved response in detecting stress changes is shown in comparison with other previous works.This work was supported in part by the Computational Intelligence Group of the University of the Basque Country, under the project IT874-13 granted by the Basque Regional Government (GV- EJ). The work has also been funded by the Jesús de Gangoiti Barrera Foundation through an specific gran

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