scholarly journals Embedded System Based on an ARM Microcontroller to Analyze Heart Rate Variability in Real Time Using Wavelets

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Victor H. Rodriguez ◽  
Carlos Medrano ◽  
Inmaculada Plaza
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Embedded System ◽  
Real Time ◽  
Qrs Complex ◽  
Local Networks ◽  
Time Frequency ◽  
Hrv Analysis ◽  
Time Required ◽  
Processing Techniques

The analyses of electrocardiogram (ECG) and heart rate variability (HRV) are of primordial interest for cardiovascular diseases. The algorithm used for the detection of the QRS complex is the basis for HRV analysis and HRV quality will depend strongly on it. The aim of this paper is to implement HRV analysis in real time on an ARM microcontroller (MCU). Thus, there is no need to send raw data to a cloud server for real time HRV monitoring and, consequently, the communication requirements and the power consumption of the local sensor node would be far lower. The system would facilitate the integration into edge computing, for instance, in small local networks, such as hospitals. A QRS detector based on wavelets is proposed, which is able to autonomously select the coefficients the QRS complex will be detected with. To validate it, the MITBIH and NSRDB databases were used. This detector was implemented in real time using an MCU. Subsequently HRV analysis was implemented in the time, frequency, and nonlinear domains. When evaluating the QRS detector with the MITBIH database, 99.61% positive prediction (PP), 99.3% sensitivity (SE), and a prediction error rate (DER) of 1.12% were obtained. For the NSRDB database the results were a PP of 99.95%, an SE of 99.98%, and a DER of 0.0006%. The execution of the QRS detector in the MCU took 52 milliseconds. On the other hand, the time required to calculate the HRV depends on the data size, but it took only a few seconds to analyze several thousands of interbeat intervals. The results obtained for the detector were superior to 99%, so it is expected that the HRV is reliable. It has also been shown that the detection of QRS complex can be done in real time using advanced processing techniques such as wavelets.

Detecting Pedestrian Situation Awareness in Real-Time: Algorithm Development Using Heart Rate Variability and Phone Position

2018 ◽  
Vol 62 (1) ◽  
pp. 1579-1583
Author(s):  
Yourui Tong ◽  
Bochen Jia ◽  
Yi Wang ◽  
Si Yang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
Situation Awareness ◽  
Mental State ◽  
Time Algorithm ◽  
Support Vector ◽  
Vehicle Design ◽  
Automated Vehicle ◽  
Hrv Analysis

To help automated vehicles learn surrounding environments via V2X communications, it is important to detect and transfer pedestrian situation awareness to the related vehicles. Based on the characteristics of pedestrians, a real-time algorithm was developed to detect pedestrian situation awareness. In the study, the heart rate variability (HRV) and phone position were used to understand the mental state and distractions of pedestrians. The HRV analysis was used to detect the fatigue and alert state of the pedestrian, and the phone position was used to define the phone distractions of the pedestrian. A Support Vector Machine algorithm was used to classify the pedestrian’s mental state. The results indicated a good performance with 86% prediction accuracy. The developed algorithm shows high applicability to detect the pedestrian’s situation awareness in real-time, which would further extend our understanding on V2X employment and automated vehicle design.

Heart Rate Variability Assessment and Its Application for Autonomic Function Evaluation in Patients with Hyperhidrosis

2020 ◽  
Vol 83 (3) ◽  
pp. 293-300
Author(s):  
Aracy Satoe Mautari Niwa ◽  
Michele Lima Gregório ◽  
Luiz Eduardo Villaça Leão ◽  
Moacir Fernandes de Godoy
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Point Of View ◽  
Function Evaluation ◽  
Recurrence Plots ◽  
Time Frequency ◽  
Hrv Analysis ◽  
Focal Hyperhidrosis ◽  
Systemic Component

Background: Pathophysiology mechanism of primary focal hyperhidrosis (PFHH) is controversial. Heart rate variability (HRV) could explain if there is a systemic component present. We aimed to investigate the functions of the autonomic nervous system in patients diagnosed with PFHH compared to controls using the analysis of HRV in the domains of time, frequency, and nonlinearity, as well as analysis of the recurrence plots (RPs). Methods: We selected 34 patients with PFHH (29.4 ± 10.2 years) and 34 controls (29.2 ± 9.6 years) for HRV analysis. Heart beats were recorded with Polar RS800CX monitor (20 min, at rest, in supine position), and RR intervals were analyzed with Kubios Premium HRV software. RPs were constructed with Visual Recurrence Analysis software. Statistical analysis included unpaired t test (p < 0.05). Results: Our results showed that HRV parameters in the 3 domains evaluated did not show any differences between the groups. The same was observed with RPs. Conclusions: The findings suggest that PFHH, from the pathophysiological point of view, may be caused by peripheral involvement of the sympathetic nervous system (glandular level or nerve terminals), as there was no difference between the groups studied. More specific studies should help elucidate this issue.

scholarly journals Is Ultra-Short-Term Heart Rate Variability Valid in Non-static Conditions?

2021 ◽  
Vol 12 ◽  
Author(s):  
Jin Woong Kim ◽  
Hyeon Seok Seok ◽  
Hangsik Shin
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Measurement Time ◽  
Exercise Recovery ◽  
Short Term ◽  
Post Exercise ◽  
Hrv Analysis ◽  
Time Required ◽  
Static Conditions ◽  
Post Exercise Recovery

In mobile healthcare, heart rate variability (HRV) is increasingly being used in dynamic patient states. In this situation, shortening of the measurement time is required. This study aimed to validate ultra-short-term HRV in non-static conditions. We conducted electrocardiogram (ECG) measurements at rest, during exercise, and in the post-exercise recovery period in 30 subjects and analyzed ultra-short-term HRV in time and frequency domains by ECG in 10, 30, 60, 120, 180, and 240-s intervals, and compared the values to the 5-min HRV. For statistical analysis, null hypothesis testing, Cohen’s d statistics, Pearson’s correlation coefficient, and Bland-Altman analysis were used, with a statistical significance level of P &lt; 0.05. The feasibility of ultra-short-term HRV and the minimum time required for analysis showed differences in each condition and for each analysis method. If the strict criteria satisfying all the statistical methods were followed, the ultra-short-term HRV could be derived from a from 30 to 240-s length of ECG. However, at least 120 s was required in the post-exercise recovery or exercise conditions, and even ultra-short-term HRV was not measurable in some variables. In contrast, according to the lenient criteria needed to satisfy only one of the statistical criteria, the minimum time required for ultra-short-term HRV analysis was 10–60 s in the resting condition, 10–180 s in the exercise condition, and 10–120 s in the post-exercise recovery condition. In conclusion, the results of this study showed that a longer measurement time was required for ultra-short-term HRV analysis in dynamic conditions. This suggests that the existing ultra-short-term HRV research results derived from the static condition cannot applied to the non-static conditions of daily life and that a criterion specific to the non-static conditions are necessary.

scholarly journals Stationary Bike Training Performance Using Heart Rate Variability

10.29007/j6zx ◽  
2020 ◽  
Author(s):  
Quoc Cuong Pham ◽  
Tran Duc Minh Nguyen ◽  
Dang Le Cao ◽  
Quoc Khai Le ◽  
Quang Linh Huynh
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Training Performance ◽  
Time Frequency ◽  
Effective Training ◽  
Hrv Analysis ◽  
And Performance ◽  
Training Exercises ◽  
Response To Exercise ◽  
Electrocardiogram Ecg

Exercising is said to bring benefits to people taking part in, not only physical but also physiological gain. Heart Rate Variability (HRV) is an important marker reflecting the function of the autonomic nervous system (ANS), which has shown potentials in some exercise therapy and sport physiology studies. HRV analysis is said to be used for getting a better understanding of our body’s response to exercise and the reaction to different stressors from the workout. Thus, it is essential to monitor and optimize the recovery to avoid overtraining. This study aims to investigate the influence of HRV reflecting the physical stress level on participants when exercising, therefore, building a concept of self-training guide to improve the adaptation and performance. Electrocardiogram (ECG) is acquired by the BIOPAC system over 10 healthy college students during a proposed training protocol on the stationary bike, and post-exercising. HRV data from ECG is analyzed in time, frequency and nonlinear domains to extract various features to evaluate physiological recovery status, manage physical fatigue, intensity adjustment. From the evaluation of these indexes, participants are able to keep track of their physiological condition as well as to have more effective training exercises.

scholarly journals Exercise Physiology: Improving Stationary Bike Training Performance Using Heart Rate Variability

10.29007/2s7r ◽  
2020 ◽  
Author(s):  
Quoc Cuong Pham ◽  
Tran Duc Minh Nguyen ◽  
Dang Le Cao ◽  
Quoc Khai Le ◽  
Quang Linh Huynh
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Exercise Physiology ◽  
Training Performance ◽  
Time Frequency ◽  
Hrv Analysis ◽  
And Performance ◽  
Training Exercises ◽  
Response To Exercise ◽  
Electrocardiogram Ecg

Exercising is said to bring benefits to people taking part in, not only physical but also physiological gain. Heart Rate Variability (HRV) is an important marker reflecting the function of the autonomic nervous system (ANS), which has shown potentials in some exercise therapy and sport physiology studies. HRV analysis is said to be used for getting a better understanding of our body’s response to exercise and the reaction to different stressors from the workout. Thus, it is essential to monitor and optimize the recovery to avoid overtraining. This study aims to investigate the influence of HRV reflecting the physical stress level on participants when exercising, therefore, building a concept of self-training guide to improve the adaptation and performance. Electrocardiogram (ECG) is acquired by the BIOPAC system over 10 healthy college students during a proposed training protocol on the stationary bike, and post-exercising. HRV data from ECG is analyzed in time, frequency and nonlinear domains to extract various features to evaluate physiological recovery status, manage physical fatigue, intensity adjustment. From the evaluation of these indexes, participants are able to keep track of their physiological condition as well as to have more effective training exercises.

An Examination of Real-Time Heart Rate Variability During Laparoscopic Cholecystectomies and Radical Surgeries

2000 ◽  
Author(s):  
K. Zaglaniczny ◽  
W. Shoemaker ◽  
D. S. Gorguze ◽  
C. Woo ◽  
J. Colombo
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time

scholarly journals Field Monitoring the Effects of Overnight Shift Work on Specialist Tactical Police Training with Heart Rate Variability Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 7895
Author(s):  
Colin Tomes ◽  
Ben Schram ◽  
Robin Orr
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Shift Work ◽  
Emergency Response ◽  
Police Training ◽  
Heart Rate Variability Analysis ◽  
Response Team ◽  
Hrv Analysis ◽  
And Performance ◽  
Personnel Allocation

Police work exposes officers to high levels of stress. Special emergency response team (SERT) service exposes personnel to additional demands. Specifically, the circadian cycles of SERT operators are subject to disruption, resulting in decreased capacity to compensate in response to changing demands. Adaptive regulation loss can be measured through heart rate variability (HRV) analysis. While HRV Trends with health and performance indicators, few studies have assessed the effect of overnight shift work on HRV in specialist police. Therefore, this study aimed to determine the effects overnight shift work on HRV in specialist police. HRV was analysed in 11 SERT officers and a significant (p = 0.037) difference was found in pRR50 levels across the training day (percentage of R-R intervals varying by >50 ms) between those who were off-duty and those who were on duty the night prior. HRV may be a valuable metric for quantifying load holistically and can be incorporated into health and fitness monitoring and personnel allocation decision making.

scholarly journals Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales

2017 ◽  
Vol 123 (2) ◽  
pp. 344-351 ◽  
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Renata Maria Lataro ◽  
Jaci Airton Castania ◽  
Carlos Alberto Aguiar Silva ◽  
Helio Cesar Salgado ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Multiscale Entropy ◽  
Time Frequency ◽  
Cardiac Control ◽  
Long Time ◽  
Nonlinear Features ◽  
Short Time

Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might fail to account for the complexity of the HRV dynamics. Even though the prevalent notion is that HRV is nonlinear, the actual presence of nonlinear features is rarely verified. In this study, the presence of nonlinear dynamics was checked as a function of time scales in three experimental models of rats with different impairment of the cardiac control: namely, rats with heart failure (HF), spontaneously hypertensive rats (SHRs), and sinoaortic denervated (SAD) rats. Multiscale entropy (MSE) and refined MSE (RMSE) were chosen as the discriminating statistic for the surrogate test utilized to detect nonlinearity. Nonlinear dynamics is less present in HF animals at both short and long time scales compared with controls. A similar finding was found in SHR only at short time scales. SAD increased the presence of nonlinear dynamics exclusively at short time scales. Those findings suggest that a working baroreflex contributes to linearize HRV and to reduce the likelihood to observe nonlinear components of the cardiac control at short time scales. In addition, an increased sympathetic modulation seems to be a source of nonlinear dynamics at long time scales. Testing nonlinear dynamics as a function of the time scales can provide a characterization of the cardiac control complementary to more traditional markers in time, frequency, and information domains. NEW & NOTEWORTHY Although heart rate variability (HRV) dynamics is widely assumed to be nonlinear, nonlinearity tests are rarely used to check this hypothesis. By adopting multiscale entropy (MSE) and refined MSE (RMSE) as the discriminating statistic for the nonlinearity test, we show that nonlinear dynamics varies with time scale and the type of cardiac dysfunction. Moreover, as complexity metrics and nonlinearities provide complementary information, we strongly recommend using the test for nonlinearity as an additional index to characterize HRV.

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