HRVTool - an Open-Source Matlab Toolbox for Analyzing Heart Rate Variability

Marcus, Vollmer

doi:10.22489/cinc.2019.032

Cited by 80 publications

(64 citation statements)

References 8 publications

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“…We created an artifact handling tool in MATLAB with the batch processing capacity to solve this intricacy. MATLAB built-in tools and Marcus Vollmer's HRV toolbox [19] are used for processing the heart activity signal. First, with 50 percent overlapping, the data is split into 2 minutes long segments.…”

Section: ) Heart Activity Preprocessing Artifact Detection Andmentioning

confidence: 99%

Real-Life Stress Level Monitoring Using Smart Bands in the Light of Contextual Information

et al. 2020

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An automatic stress detection system that uses unobtrusive smart bands will contribute to human health and well-being by alleviating the effects of high stress levels. However, there are a number of challenges for detecting stress in unrestricted daily life which results in lower performances of such systems when compared to semi-restricted and laboratory environment studies. The addition of contextual information such as physical activity level, activity type and weather to the physiological signals can improve the classification accuracies of these systems. We developed an automatic stress detection system that employs smart bands for physiological data collection. In this study, we monitored the stress levels of 16 participants of an EU project training every day throughout the eight days long event by using our system. We collected 1440 hours of physiological data and 2780 self-report questions from the participants who are from diverse countries. The project midterm presentations (see Figure 3) in front of a jury at the end of the event were the source of significant real stress. Different types of contextual information, along with the physiological data, were recorded to determine the perceived stress levels of individuals. We further analyze the physiological signals in this event to infer long term perceived stress levels which we obtained from baseline PSS-14 questionnaires. Session-based, daily and long-term perceived stress levels could be identified by using the proposed system successfully.

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Section: ) Heart Activity Preprocessing Artifact Detection Andmentioning

confidence: 99%

Real-Life Stress Level Monitoring Using Smart Bands in the Light of Contextual Information

et al. 2020

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“…We extracted time and frequency domain heart rate variability features from the segmented time windows. We used Marcus Volmer's toolbox [42] which is implemented in MATLAB. We selected the features which are commonly used in the previous works related to heart rate variability [9], [41] and [43].…”

Section: Feature Extractionmentioning

confidence: 99%

Can a Smartband be Used for Continuous Implicit Authentication in Real Life

et al. 2020

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The use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution, which will make these systems less vulnerable to the spoofing attacks. Physiological signals can be used for continuous authentication due to their uniqueness. Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors, which enable us to extract heart rate variability (HRV) features. In this study, we show that these devices can be used for continuous physiological authentication for enhancing the security of the cloud, edge services, and IoT devices. A system that is suitable for the smartband framework comes with new challenges such as relatively low signal quality and artifacts due to placement, which were not encountered in full lead electrocardiogram systems. After the artifact removal, cleaned physiological signals are fed to the machine learning algorithms. In order to train our machine learning models, we collected physiological data using off-the-shelf smartbands and smartwatches in a real-life event. By applying a minimum quality threshold, we achieved a 3.96% Equal Error Rate. Performance evaluation shows that HRV is a strong candidate for continuous unobtrusive implicit physiological authentication.

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“…These regulatory mechanisms include respiratory-driven acceleration and deceleration of the heart rate (respiratory sinus arrhythmia, RSA) via the nervus vagus, the baroreceptor reflex for short-term blood pressure regulation, and rhythmic changes in the vascular tone. In addition, a variety of excellent non-commercial and commercial software are available, which provide the quantification of inter-beat intervals (R–R intervals) and a—for the most part automatic—analysis of heart rate and heart rate variability (HRV) patterns in short- and long-term recordings [ 4 , 5 , 6 , 7 ]. In other words, using smart mobile devices, HRV provides a seemingly simple opportunity to examine the interaction between sympathetic and parasympathetic nervous system activities non-invasively, which may supply with useful information about a number of physiological situations.…”

Section: Introductionmentioning

confidence: 99%

How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG

Lackner

Eglmaier

Hackl-Wimmer

et al. 2020

Sensors

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Recent developments in noninvasive electrocardiogram (ECG) monitoring with small, wearable sensors open the opportunity to record high-quality ECG over many hours in an easy and non-burdening way. However, while their recording has been tremendously simplified, the interpretation of heart rate variability (HRV) data is a more delicate matter. The aim of this paper is to supply detailed methodological discussion and new data material in order to provide a helpful notice of HRV monitoring issues depending on recording conditions and study populations. Special consideration is given to the monitoring over long periods, across periods with different levels of activity, and in adults versus children. Specifically, the paper aims at making users aware of neglected methodological limitations and at providing substantiated recommendations for the selection of appropriate HRV variables and their interpretation. To this end, 30-h HRV data of 48 healthy adults (18–40 years) and 47 healthy toddlers (16–37 months) were analyzed in detail. Time-domain, frequency-domain, and nonlinear HRV variables were calculated after strict signal preprocessing, using six different high-frequency band definitions including frequency bands dynamically adjusted for the individual respiration rate. The major conclusion of the in-depth analyses is that for most applications that implicate long-term monitoring across varying circumstances and activity levels in healthy individuals, the time-domain variables are adequate to gain an impression of an individual’s HRV and, thus, the dynamic adaptation of an organism’s behavior in response to the ever-changing demands of daily life. The sound selection and interpretation of frequency-domain variables requires considerably more consideration of physiological and mathematical principles. For those who prefer using frequency-domain variables, the paper provides detailed guidance and recommendations for the definition of appropriate frequency bands in compliance with their specific recording conditions and study populations.

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HRVTool - an Open-Source Matlab Toolbox for Analyzing Heart Rate Variability

Cited by 80 publications

References 8 publications

Real-Life Stress Level Monitoring Using Smart Bands in the Light of Contextual Information

Real-Life Stress Level Monitoring Using Smart Bands in the Light of Contextual Information

Can a Smartband be Used for Continuous Implicit Authentication in Real Life

How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG

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