scholarly journals 1E1-2　Analysis on Heart Rate Variability using Wearable Devices

2018 ◽  
Vol 54 (Supplement) ◽  
pp. 1E1-2-1E1-2
Author(s):  
Emi HAYASHI ◽  
Kiyoko YOKOYAMA ◽  
Hisatoshi ITO ◽  
Yuko KAWAHARA
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wearable Devices

The effect of heart rate variability biofeedback training on stress and anxiety: a meta-analysis

2017 ◽  
Vol 47 (15) ◽  
pp. 2578-2586 ◽  
Author(s):  
V. C. Goessl ◽  
J. E. Curtiss ◽  
S. G. Hofmann
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Meta Analysis ◽  
Wearable Devices ◽  
Biofeedback Training ◽  
Cochrane Library ◽  
Heart Rate Variability Biofeedback ◽  
Stress Symptoms ◽  
The Cochrane Library ◽  
Study Bias

BackgroundSome evidence suggests that heart rate variability (HRV) biofeedback might be an effective way to treat anxiety and stress symptoms. To examine the effect of HRV biofeedback on symptoms of anxiety and stress, we conducted a meta-analysis of studies extracted from PubMed, PsycINFO and the Cochrane Library.MethodsThe search identified 24 studies totaling 484 participants who received HRV biofeedback training for stress and anxiety. We conducted a random-effects meta-analysis.ResultsThe pre-post within-group effect size (Hedges' g) was 0.81. The between-groups analysis comparing biofeedback to a control condition yielded Hedges' g = 0.83. Moderator analyses revealed that treatment efficacy was not moderated by study year, risk of study bias, percentage of females, number of sessions, or presence of an anxiety disorder.ConclusionsHRV biofeedback training is associated with a large reduction in self-reported stress and anxiety. Although more well-controlled studies are needed, this intervention offers a promising approach for treating stress and anxiety with wearable devices.

scholarly journals A wearable system for real-time outpatient ECG monitoring

2019 ◽  
pp. 26-30
Author(s):  
T.O. Білобородова ◽  
І.С. Скарга-Бандурова ◽  
В.С. Дерев’янченко
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Monitoring System ◽  
Morphological Feature ◽  
Well Being ◽  
Wearable Devices ◽  
Ecg Signal ◽  
Ecg Monitoring ◽  
Systems Architecture

Functional state of the cardiovascular system is an important factor for human physical well-being. To perform analysis of the cardiovascular state, the wearable continuous ECG monitoring system is essential. In this paper, a wearable ECG monitoring system based on IoT is proposed. The systems architecture is presented. Wearable devices design employs few optimal components for the acquisition of acceptable ECG signal. The R peaks corresponding to each heartbeat, and T waves, a morphological feature of the ECG are detected. It enables to perform heart rate and heart rate variability analyses, as well as  extract, store and analyze  the long term ECG measurements.

scholarly journals Assessment of physiological signs associated with COVID-19 measured using wearable devices

2020 ◽  
Author(s):  
Aravind Natarajan ◽  
Hao-Wei Su ◽  
Conor Heneghan
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Respiration Rate ◽  
Demographic Data ◽  
Wearable Devices ◽  
Ventilation Support ◽  
Consumer Devices ◽  
Variability Data ◽  
Logistic Regression Classifier ◽  
At Home

Respiration rate, heart rate, and heart rate variability are some health metrics that are easily measured by consumer devices and which can potentially provide early signs of illness. Furthermore, mobile applications which accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic. We considered two approaches to assessing COVID-19 - a symptom-based approach, and a physiological signs based technique. Firstly, we trained a Logistic Regression classifier to predict the need for hospitalization of COVID-19 patients given the symptoms experienced, age, sex, and BMI. Secondly, we trained a neural network classifier to predict whether a person is sick on any specific day given respiration rate, heart rate, and heart rate variability data for that day and and for the four preceding days. Data on 1,181 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 - July 14, 2020. 11.0% of COVID-19 subjects were asymptomatic, 47.2% of subjects recovered at home by themselves, 33.2% recovered at home with the help of someone else, 8.16% of subjects required hospitalization without ventilation support, and 0.448% required ventilation. Fever was present in 54.8% of subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.77 +/- 0.05 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 +/- 0.03 for the prediction of illness on a specific day with 4 previous days of history. Respiration rate and heart rate are typically elevated by illness, while heart rate variability is decreased. Measuring these metrics can help in early diagnosis, and in monitoring the progress of the disease.

Personal Wearable Devices to Measure Heart Rate Variability

2017 ◽  
Author(s):  
Kelvin K.F. Tsoi ◽  
Janet Y.H. Wong ◽  
Michael P.F. Wong ◽  
Gary K.S. Leung ◽  
Baker K.K. Bat ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wearable Devices ◽  
Measure Heart Rate

scholarly journals Can Wearable Devices Accurately Measure Heart Rate Variability? A Systematic Review

Folia Medica ◽  
2018 ◽  
Vol 60 (1) ◽  
Author(s):  
Konstantinos Georgiou ◽  
Andreas V. Larentzakis ◽  
Nehal N. Khamis ◽  
Ghadah I. Alsuhaibani ◽  
Yasser A. Alaska ◽  
...  
Keyword(s):  
Systematic Review ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Wearable Devices ◽  
Measure Heart Rate

scholarly journals Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7233
Author(s):  
Jayroop Ramesh ◽  
Zahra Solatidehkordi ◽  
Raafat Aburukba ◽  
Assim Sagahyroon
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Gold Standard ◽  
Normal Sinus Rhythm ◽  
Binary Classification ◽  
Wearable Devices ◽  
Deep Convolutional Neural Networks ◽  
Pulse Rate Variability ◽  
Medical Grade

Atrial fibrillation (AF) is a type of cardiac arrhythmia affecting millions of people every year. This disease increases the likelihood of strokes, heart failure, and even death. While dedicated medical-grade electrocardiogram (ECG) devices can enable gold-standard analysis, these devices are expensive and require clinical settings. Recent advances in the capabilities of general-purpose smartphones and wearable technology equipped with photoplethysmography (PPG) sensors increase diagnostic accessibility for most populations. This work aims to develop a single model that can generalize AF classification across the modalities of ECG and PPG with a unified knowledge representation. This is enabled by approximating the transformation of signals obtained from low-cost wearable PPG sensors in terms of Pulse Rate Variability (PRV) to temporal Heart Rate Variability (HRV) features extracted from medical-grade ECG. This paper proposes a one-dimensional deep convolutional neural network that uses HRV-derived features for classifying 30-s heart rhythms as normal sinus rhythm or atrial fibrillation from both ECG and PPG-based sensors. The model is trained with three MIT-BIH ECG databases and is assessed on a dataset of unseen PPG signals acquired from wrist-worn wearable devices through transfer learning. The model achieved the aggregate binary classification performance measures of accuracy: 95.50%, sensitivity: 94.50%, and specificity: 96.00% across a five-fold cross-validation strategy on the ECG datasets. It also achieved 95.10% accuracy, 94.60% sensitivity, 95.20% specificity on an unseen PPG dataset. The results show considerable promise towards seamless adaptation of gold-standard ECG trained models for non-ambulatory AF detection with consumer wearable devices through HRV-based knowledge transfer.

scholarly journals Wearable Devices Suitable for Monitoring Twenty Four Hour Heart Rate Variability in Military Populations

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1061
Author(s):  
Katrina Hinde ◽  
Graham White ◽  
Nicola Armstrong
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Wearable Devices ◽  
Health Issues ◽  
Autonomic Nervous ◽  
Military Populations ◽  
Hrv Analysis ◽  
Field Setting

Heart rate variability (HRV) measurements provide information on the autonomic nervous system and the balance between parasympathetic and sympathetic activity. A high HRV can be advantageous, reflecting the ability of the autonomic nervous system to adapt, whereas a low HRV can be indicative of fatigue, overtraining or health issues. There has been a surge in wearable devices that claim to measure HRV. Some of these include spot measurements, whilst others only record during periods of rest and/or sleep. Few are capable of continuously measuring HRV (≥24 h). We undertook a narrative review of the literature with the aim to determine which currently available wearable devices are capable of measuring continuous, precise HRV measures. The review also aims to evaluate which devices would be suitable in a field setting specific to military populations. The Polar H10 appears to be the most accurate wearable device when compared to criterion measures and even appears to supersede traditional methods during exercise. However, currently, the H10 must be paired with a watch to enable the raw data to be extracted for HRV analysis if users need to avoid using an app (for security or data ownership reasons) which incurs additional cost.

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