Classification of Physiological States Through Machine Learning Algorithms Applied to Ultra-Short-Term Heart Rate and Pulse Rate Variability Indices on a Single-Feature Basis

This study investigates the feasibility of classifying physiological stress states usingMachine Learning (ML) algorithms on short-term (ST,∼5min) and ultra-short-term (UST, < 5 min, down to 10 heartbeats) heart rate (HRV) or pulse rate variability (PRV) features computed from inter-beat interval time series. Three widely employed ML algorithms were used, i.e. Naive Bayes Classifier, Support Vector Machines, and Neural Networks, on various time-, frequency and information domain HRV/PRV indices on a single-feature basis. Data were collected from healthy individuals during different physiological states including rest, postural and mental stress. Results highlighted comparable values using either HRV or PRV indices, and higher accuracy (>65% for most features and all classifiers) when classifying postural than mental stress. While decreasing the time series length, time-domain indices resulted still reliable down to ∼10 s, contrary to UST frequency-domain features which reported lower accuracy below 60 heartbeats

System to Recommend the Best Place to Live Based on Wellness State of the User Employing the Heart Rate Variability

[EN] The conditions of the environment where a person lives have a great impact on his wellness state. When buying a new house, it is important to select a place that aids in improving the wellness state of the buyer or, at least, keeps it at the same level. A deficient wellness state implies an increase of stress and the appearance of some effects associated with it. Heart rate variability (HRV) allows measuring the stress or wellness levels of a person by measuring the difference in time between heartbeats. A low HRV is related to high stress levels whereas a high HRV is associated with a high wellness state. In this paper, we present a system that measures the wellness and stress levels of home buyers by employing sensors that measure the HRV. Our system is able to process the data and recommend the best neighborhood to live in considering the wellness state of the buyer. Several tests were performed utilizing different locations. In order to determine the best neighborhood, we have developed an algorithm that assigns different values to the area in accordance with the HRV measures. Results show that the system is effective in providing the recommendation of the place that would allow the person to live with the highest wellness state.Lacuesta Gilabert, R.; García-García, L.; García-Magariño, I.; Lloret, J. (2017). System to Recommend the Best Place to Live Based on Wellness State of the User Employing the Heart Rate Variability. IEEE Access. 5:10594-10604. doi:10.1109/ACCESS.2017.2702107S1059410604

Past strong experiences determine acute cardiovascular autonomic responses to acoustic stress

Background: Stress is a major risk factor for cardiovascular (CV) disease. We hypothesized that past strong experiences might modulate acute CV autonomic responses to an unexpected acoustic stimulus. A i m: The study’s aim was to compare acute CV autonomic responses to acoustic stress between students with and without a past strong experience associated with the acoustic stimulus. Materials and Methods: Twenty five healthy young volunteers - medical and non-medical students - were included in the study. CV hemodynamic parameters, heart rate (HR), and blood pressure (BP) variability were assessed for 10 min at rest and for 10 min after two different acoustic stimuli: a standard sound signal and a specific sound signal used during a practical anatomy exam (so-called "pins"). Results: Both sounds stimulated the autonomic nervous system. The "pins" signal caused a stronger increase in HR in medical students (69 ± 10 vs. 73 ± 13 bpm, p = 0.004) when compared to non-medical students (69 ± 6 vs. 70 ± 10, p = 0.695). Rises in diastolic BP, observed 15 seconds after sound stressors, were more pronounced after the "pins" sound than after the standard sound signal only in medical students (3.1% and 1.4% vs. 3% and 4.4%), which was also reflected by low-frequency diastolic BP variability (medical students: 6.2 ± 1.6 vs. 4.1 ± 0.8 ms2, p = 0.04; non-medical students: 6.0 ± 4.3 vs. 4.1 ± 2.6 ms2, p = 0.06). Conclusions: The "pins" sound, which medical students remembered from their anatomy practical exam, provoked greater sympathetic activity in the medical student group than in their non-medical peers. Thus, past strong experiences modulate CV autonomic responses to acute acoustic stress

Personalization of convolutional neural networks within the stress detection task using heart rate variability data

Stress detection is an active area of research with important implications for personal, occupational, and social health. Most modern approaches use features computed from multiple sensor modalities, i.e., grouping different types of data from multiple sources for processing. These include electrocardiogram, electrodermal activity, electromyogram, skin temperature, respiration, accelerometer data, etc. Also, traditional machine learning algorithms (decision tree, discriminant analysis, support vector machine, etc.) or fully-connected neural networks are mostly used. Using these methods requires large amounts of data. Researchers are considering different approaches to personalization or generalization of models relative to subjects, namely subject-independent and subject-dependent (initially personal or adapted) models. The aim of the presented work is to develop a method for detecting stress based on heart rate variability data, taking into account the process of personalization of neural networks. The use of a convolutional neural network is proposed. The dependence of accuracy on the length of the input signal is studied. The dependence of accuracy on the data dimensionality reduction layer (one-dimensional convolutional layer, maximizing and averaging pooling) used in the network is also considered. The importance of personalizing models is demonstrated to significantly increase the accuracy of models of specific subjects. It is shown that the proposed method, based on 60 intervals between heartbeats, makes it possible to binary determine whether a person is under stress. Personalization allowed increasing the accuracy from 91.8 % to 98.9 ± 2.6 %. The F1-score value increased from 0.907 to 0.983 ± 0.038. The proposed personalized networks can be used in systems for monitoring the functional state of a person. They can also be used as part of a system that grants or restricts access to private resources based on whether a person is currently at rest

Ethics and biomedical engineering for well-being : a cocreation study of remote services for monitoring and support

The well-being of students and staff directly affects their output and efficiency. This study presents the results of two focus groups conducted in 2022 within a two-phase project led by the Applied Biomedical and Signal Processing Intelligent e-Health Lab, School of Engineering at the University of Warwick, and British Telecom within “The Connected Campus: University of Warwick case study” program. The first phase, by involving staff and students at the University of Warwick, aimed at collecting preliminary information for the subsequent second phase, about the feasibility of the use of Artificial Intelligence and Internet of Things for well-being support on Campus. The main findings of this first phase are interesting technological suggestions from real users. The users helped in the design of the scenarios and in the selection of the key enabling technologies which they considered as the most relevant, useful and acceptable to support and improve well-being on Campus. These results will inform future services to design and implement technologies for monitoring and supporting well-being, such as hybrid, minimal and even intrusive (implantable) solutions. The user-driven co-design of such services, leveraging the use of wearable devices and Artificial Intelligence deployment will increase their acceptability by the users

How to Relax in Stressful Situations: A Smart Stress Reduction System

Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual's health, well-being and socio-economic situation. Stress management application development for wearable smart devices is a growing market. The use of wearable smart devices and biofeedback for individualized real-life stress reduction interventions has received less attention. By using our unobtrusive automatic stress detection system for use with consumer-grade smart bands, we first detected stress levels. When a high stress level is detected, our system suggests the most appropriate relaxation method by analyzing the physical activity-based contextual information. In more restricted contexts, physical activity is lower and mobile relaxation methods might be more appropriate, whereas in free contexts traditional methods might be useful. We further compared traditional and mobile relaxation methods by using our stress level detection system during an eight day EU project training event involving 15 early stage researchers (mean age 28; gender 9 Male, 6 Female). Participants' daily stress levels were monitored and a range of traditional and mobile stress management techniques was applied. On day eight, participants were exposed to a 'stressful' event by being required to give an oral presentation. Insights about the success of both traditional and mobile relaxation methods by using the physiological signals and collected self-reports were provided

Heart Rate Variability (HRV) analysis : a methodology for organizational neuroscience

Recently, the application of neuroscience methods and findings to the study of organizational phenomena has gained significant interest and converged in the emerging field of organizational neuroscience. Yet, this body of research has principally focused on the brain, often overlooking fuller analysis of the activities of the human nervous system and associated methods available to assess them. In this paper, we aim to narrow this gap by reviewing heart rate variability (HRV) analysis, which is that set of methods assessing beat-to-beat changes in the heart rhythm over time, used to draw inference on the outflow of the autonomic nervous system (ANS). In addition to anatomo- physiological and detailed methodological considerations, we discuss related theoretical, ethical, and practical implications. Overall, we argue that this methodology offers the opportunity not only to inform on a wealth of constructs relevant for management inquiries, but also to advance the organizational neuroscience research agenda and its ecological validity