This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress. ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman’s rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features. Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier. This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation.

/pdf/ultra-short-term-hrv-features-as-surrogates-of-short-term-f4hvb9x7pk.pdf

Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life

The online telemedicine systems are helpful since they provide timely and effective healthcare services. Such online healthcare systems are usually based on sophisticated and advanced wearable and wireless sensor technologies. A rapid technological growth has improved the scope of many remote health monitoring systems. Here, the researchers employed a cloud-based remote monitoring system for observing the health status of the patients after monitoring their heart rate variability. This system was developed after considering many factors like the ease of application, costs, accuracy, and the data security. Furthermore, this system was also conceptualized to act as an interface between the patients and the healthcare providers, thus ensuring a two-way communication between them. The major aim of this paper was to provide the best healthcare monitoring services to the people living in the remote areas, which was otherwise very difficult owing to the small doctor-to-patient ratio. The researchers also analyzed their monitoring system using two different databases. First comes from MIT Physionet database i.e., the MIT-BIH sinus rhythm and the MIT-St. Petersburg. While the second database was collected after monitoring 30 people who were asked to use these wearable sensors. After analyzing the performance of the proposed scheme, the obtained results for accuracy, sensitivity, and specificity were 99.02%, 98.78%, and 99.17%, respectively. The achieved results concluded that the proposed system was quite reliable, robust, and valuable. Also, the data analysis revealed that this system was very convenient and ensured data security. In addition, this developed monitoring system generated warning messages, directed towards the patients and the doctors, during some critical situation.

An Automated Remote Cloud-Based Heart Rate Variability Monitoring System

Suffering from continuous stress can lead to serious psychological and even physical disorders. Objective stress assessment methods using noninvasive physiological responses such as heart rate variability (HRV) and electroencephalograms (EEG) have therefore been proposed for effective stress management. In this study, a novel wearable device that can measure electrocardiograms (ECG) and EEG simultaneously was designed to enable continuous stress monitoring in daily life. The developed system is easily worn by hanging from both ears, is lightweight (i.e., 42.5 g), and exhibits an excellent noise performance of 0.12 μVrms. Significant time and frequency features of HRV and EEG were found in two different stressors, namely the Stroop color word and mental arithmetic tests, using 14 young subjects. Stressor situations were classified using various HRV and EEG feature selections and a support vector machine technique. The five-fold cross-validation results obtained when using both EEG and HRV features showed the best performance with an accuracy of 87.5%, which demonstrated the requirement for simultaneous HRV and EEG measurements.

https://www.mdpi.com/1424-8220/19/9/1991/pdf

A Novel Wearable EEG and ECG Recording System for Stress Assessment.

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 article, 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 overarching organizational neuroscience res...

/pdf/heart-rate-variability-hrv-analysis-a-methodology-for-2rlkjj4mr9.pdf

Heart Rate Variability (HRV) Analysis A Methodology for Organizational Neuroscience

The autonomic nervous system plays a major role in optimising function of the cardiovascular (CV) system, which in turn has important implications for CV health Heart rate variability (HRV) is a measurable reflection of this balance between sympathetic and parasympathetic tone and has been used as a marker for cardiac status and predicting CV outcomes Recently, the availability of commercially available heart rate (HR) monitoring systems has had important CV health implications and permits ambulatory CV monitoring on a scale not achievable with traditional cardiac diagnostics The focus of the first part of this two-part review is to summarise the physiology of HRV and to describe available technologies for HRV monitoring Part two will present HRV measures for assessing CV prognosis and athletic training

Heart Rate Variability: An Old Metric with New Meaning in the Era of using mHealth Technologies for Health and Exercise Training Guidance. Part One: Physiology and Methods

M. Caserta

Papers

Acute mental stress assessment via short term HRV analysis in healthy adults : a systematic review with meta-analysis