scholarly journals Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2846 ◽  
Author(s):  
Behnam Askarian ◽  
Kwanghee Jung ◽  
Jo Woon Chong
Keyword(s):  
Heart Rate ◽  
Signal To Noise Ratio ◽  
Signal Amplitude ◽  
Measurement Procedure ◽  
Experimental Results ◽  
Estimation Accuracy ◽  
Signal Quality ◽  
Test Results ◽  
Underwater Environment ◽  
Measurement Output

Photoplethysmography (PPG) is a commonly used in determining heart rate and oxygen saturation (SpO2). However, PPG measurements and its accuracy are heavily affected by the measurement procedure and environmental factors such as light, temperature, and medium. In this paper, we analyzed the effects of different mediums (water vs. air) and temperature on the PPG signal quality and heart rate estimation. To evaluate the accuracy, we compared our measurement output with a gold-standard PPG device (NeXus-10 MKII). The experimental results show that the average PPG signal amplitude values of the underwater environment decreased considerably (22% decrease) compared to PPG signals of dry environments, and the heart rate measurement deviated 7% (5 beats per minute on average. The experimental results also show that the signal to noise ratio (SNR) and signal amplitude decrease as temperature decreases. Paired t-test which compares amplitude and heart rate values between the underwater and dry environments was performed and the test results show statistically significant differences for both amplitude and heart rate values (p < 0.05). Moreover, experimental results indicate that decreasing the temperature from 45 °C to 5 °C or changing the medium from air to water decreases PPG signal quality, (e.g., PPG signal amplitude decreases from 0.560 to 0.112). The heart rate is estimated within 5.06 bpm deviation at 18 °C in underwater environment, while estimation accuracy decreases as temperature goes down.

scholarly journals Dynamic Light Scattering: A New Noninvasive Technology for Neonatal Heart Rate Monitoring

Neonatology ◽  
2020 ◽  
Vol 117 (3) ◽  
pp. 279-286
Author(s):  
Norani H. Gangaram-Panday ◽  
Tanja van Essen ◽  
Tom G. Goos ◽  
Rogier C.J. de Jonge ◽  
Irwin K.M. Reiss ◽  
...  
Keyword(s):  
Heart Rate ◽  
Light Scattering ◽  
Dynamic Light Scattering ◽  
Signal To Noise Ratio ◽  
Signal Amplitude ◽  
Paired Data ◽  
Data Points ◽  
Study Population ◽  
Single Sensor ◽  
Good Agreement

<b><i>Background:</i></b> Heart rate (HR) detection in premature infants using electrocardiography (ECG) is challenging due to a low signal amplitude and the fragility of the premature skin. Recently, the dynamic light scattering (DLS) technique has been miniaturized, allowing noninvasive HR measurements with a single sensor. <b><i>Objective:</i></b> The aim was to determine the accuracy of DLS for HR measurement in infants, compared to ECG-derived HR. <b><i>Methods:</i></b> Stable infants with a gestational age of ≥26 weeks, monitored with ECG, were eligible for inclusion. HR was measured with the DLS sensor at 5 different sites for 15 min each. We recorded every 10th second of the DLS-derived HR and the DLS signal-to-noise ratio (SNR), and the ECG-derived HR was extracted for analysis. Patients were randomly divided into 2 groups. In the first group, the optimal SNR cut-off value was determined and then applied to the second group to assess agreement. <b><i>Results:</i></b> HR measurements from 31 infants were analyzed. ECG-DLS paired data points were collected at the forehead, an upper extremity, the thorax, a lower extremity, and the abdomen. When applying the international accuracy standard for HR detection, DLS accuracy in the first group (<i>n</i> = 15) was optimal at the forehead (SNR cut-off 1.66). Application of this cut-off to the second group (<i>n</i> = 16) showed good agreement between DLS-derived HR and ECG-derived HR (bias –0.73 bpm; 95% limits of agreement –15.46 and 14.00 bpm) at the forehead with approximately 80% (i.e., 1,066/1,310) of all data pairs remaining. <b><i>Conclusion:</i></b> The investigated DLS sensor was sensitive to movement, overall providing less accurate HR measurements than ECG and pulse oximetry. In this study population, specific measurement sites provided excellent signal quality and good agreement with ECG-derived HR.

The influence of intention implementation on throw effectiveness of young basketball players

2014 ◽  
Vol 6 (4) ◽  
Author(s):  
Dominika Wilczynska ◽  
Patrycja Lipinska ◽  
Malgorzata Wolujewicz-Czerlonko
Keyword(s):  
Heart Rate ◽  
Test Results ◽  
Basketball Players ◽  
Physical Effort ◽  
Male And Female ◽  
Mental Preparation ◽  
Physiological Cost ◽  
State Orientation ◽  
Efficiency Test ◽  
Visualization Techniques

AbstractBackground: The purpose of the following research was to find out the influence of imaginary training based on intention implementation on throw effectiveness of young basketball players, both male and female in stressogenic situations. Individual differences (action vs state orientation) between players were also measured in this research.Material/Methods: 76 players (32 females and 44 males) in the cadet category (15-16 years old) from basketball clubs of Gdansk, Gdynia and Sopot took part in this research. In the first stage all players did a throw efficiency test ERPE 05 under two conditions, and their heart rate was measured. Then players were randomly assigned to one of two groups. The first one did imaginary training based on the influence of intention implementation for 10 days, while the other did not. After 10 days ERPE 05 test under two conditions was run again.Results: Test results showed that intention implementation does not influence effectiveness improvement in stressogenic conditions as far as state-oriented players are concerned, but it does lower the physiological cost of physical effort in form of a decreased heart rate.Conclusions: This research proves that visualization training based on implementation instructions does influence young players’ physiology and significantly lowers their heart rate under stressogenic conditions. However useful, visualization techniques used in this research still need other tests and should be applied for a longer period of time to acutely show how they affect young players’ mental preparation

Estimation of Heart Rate Variability Fluctuations by Wavelet Transform

2011 ◽  
Vol 57 (3) ◽  
pp. 395-400 ◽  
Author(s):  
Anton Popov ◽  
Yevgeniy Karplyuk ◽  
Volodymyr Fesechko
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wavelet Transform ◽  
Experimental Results ◽  
Dyadic Wavelet ◽  
Heart Rate Fluctuations ◽  
Slow Heart Rate ◽  
Dyadic Wavelet Transform

Estimation of Heart Rate Variability Fluctuations by Wavelet TransformTechnique for separate estimation of fast and slow fluctuations in the heart rate signal is developed. The orthogonal dyadic wavelet transform is used to separate the slow heart rate changes in approximation part of decomposition and fast changes in detail parts. Experimental results using the recordings from persons practicing Chi meditation demonstrated the applicability of estimation heart rate fluctuations with the proposed approach.

scholarly journals 651 Longitudinal, unobtrusive, and ecologically valid sleep metric estimation from a smart bed to predict the pathology of COVID-19

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A255-A255
Author(s):  
Dmytro Guzenko ◽  
Gary Garcia ◽  
Farzad Siyahjani ◽  
Kevin Monette ◽  
Susan DeFranco ◽  
...  
Keyword(s):  
Heart Rate ◽  
Sleep Quality ◽  
Predictive Model ◽  
Sleep Duration ◽  
Wearable Sensors ◽  
Test Results ◽  
Cloud Data ◽  
Cardiorespiratory Function ◽  
Group Data ◽  
Rich Information

Abstract Introduction Pathophysiologic responses to viral respiratory challenges such as SARS-CoV-2 may affect sleep duration, quality and concomitant cardiorespiratory function. Unobtrusive and ecologically valid methods to monitor longitudinal sleep metrics may therefore have practical value for surveillance and monitoring of infectious illnesses. We leveraged sleep metrics from Sleep Number 360 smart bed users to build a COVID-19 predictive model. Methods An IRB approved survey was presented to opting-in users from August to November 2020. COVID-19 test results were reported by 2003/6878 respondents (116 positive; 1887 negative). From the positive group, data from 82 responders (44.7±11.3 yrs.) who reported the date of symptom onset were used. From the negative group, data from 1519 responders (48.4±12.9 yrs.) who reported testing dates were used. Sleep duration, sleep quality, restful sleep duration, time to fall asleep, respiration rate, heart rate, and motion level were obtained from ballistocardiography signals stored in the cloud. Data from January to October 2020 were considered. The predictive model consists of two levels: 1) the daily probability of staying healthy calculated by logistic regression and 2) a continuous density Hidden Markov Model to refine the daily prediction considering the past decision history. Results With respect to their baseline, significant increases in sleep duration, average breathing rate, average heart rate and decrease in sleep quality were associated with symptom exacerbation in COVID-19 positive respondents. In COVID-19 negative respondents, no significant sleep or cardiorespiratory metrics were observed. Evaluation of the predictive model resulted in cross-validated area under the receiving-operator curve (AUC) estimate of 0.84±0.09 which is similar to values reported for wearable-sensors. Considering additional days to confirm prediction improved the AUC estimate to 0.93±0.05. Conclusion The results obtained on the smart bed user population suggest that unobtrusive sleep metrics may offer rich information to predict and track the development of symptoms in individuals infected with COVID-19. Support (if any):

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

scholarly journals Signal quality as Achilles’ heel of graph theory in functional magnetic resonance imaging in multiple sclerosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Johan Baijot ◽  
Stijn Denissen ◽  
Lars Costers ◽  
Jeroen Gielen ◽  
Melissa Cambron ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Signal To Noise Ratio ◽  
Pearson Correlation ◽  
Signal Quality ◽  
Network Parameters ◽  
Fmri Signal ◽  
Noise Ratio ◽  
Global And Local ◽  
The Brain ◽  
Local Brain

AbstractGraph-theoretical analysis is a novel tool to understand the organisation of the brain.We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.

Experimental Study and Finite Analysis on Flexural Capacity of Laminated Glass

2011 ◽  
Vol 243-249 ◽  
pp. 258-262
Author(s):  
Jun Chen ◽  
Jia Lv ◽  
Qi Lin Zhang ◽  
Zhi Xiong Tao ◽  
Jun Chen
Keyword(s):  
Experimental Results ◽  
Laminated Glass ◽  
Test Results ◽  
Design Code ◽  
Flexural Capacity ◽  
Equivalent Thickness ◽  
High Rise ◽  
Safety Glass ◽  
Current Design ◽  
Good Agreement

Laminated glass has been increasing widely used in high rise buildings as a kind of safety glass in recent years. So we should analyze its material property. In this paper, we use flexural experiments and ANSYS program to analyze the main factors that affect the flexural capacity of the laminated glass. The test results show that the flexural capacity is closely related to film. And the ANSYS program had got good agreement with the experimental results. Comparison of experimental results with calculated ones indicates that the current design code will lead to conservative results and the equivalent thickness of laminated glasses provided in the code should be further discussed.

scholarly journals Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Qi An ◽  
Zi-shu He ◽  
Hui-yong Li ◽  
Yong-hua Li
Keyword(s):  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Frequency Offset ◽  
Estimation Accuracy ◽  
Modulation Classification ◽  
Electronic Warfare ◽  
Wireless Communication Network ◽  
Shift Keying ◽  
Analytical Formulas ◽  
Fractional Function

Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

scholarly journals Estimating Resting Core Temperature Using Heart Rate

2018 ◽  
Vol 1 (2) ◽  
pp. 79-86 ◽  
Author(s):  
David P. Looney ◽  
Mark J. Buller ◽  
Andrei V. Gribok ◽  
Jayme L. Leger ◽  
Adam W. Potter ◽  
...  
Keyword(s):  
Heart Rate ◽  
Core Temperature ◽  
Estimation Algorithm ◽  
Quadratic Equation ◽  
Estimation Accuracy ◽  
Future Research ◽  
Accuracy And Precision ◽  
Military Populations ◽  
Thermal Work ◽  
Ct Data

ECTemp™ is a heart rate (HR)-based core temperature (CT) estimation algorithm mainly used as a real-time thermal-work strain indicator in military populations. ECTemp™ may also be valuable for resting CT estimation, which is critical for circadian rhythm research. This investigation developed and incorporated a sigmoid equation into ECTemp™ to better estimate resting CT. HR and CT data were collected over two calorimeter test trials from 16 volunteers (age, 23 ± 3 yrs; height, 1.72 ± 0.07 m; body mass, 68.5 ± 8.1 kg) during periods of sleep and inactivity. Half of the test trials were combined with ECTemp™’s original development dataset to train the new sigmoid model while the other was used for model validation. Models were compared by their estimation accuracy and precision. While both models produced accurate CT estimates, the sigmoid model had a smaller bias (−0.04 ± 0.26°C vs. −0.19 ± 0.29°C) and root mean square error (RMSE; 0.26°C vs. 0.35°C). ECTemp™ is a validated HR-based resting CT estimation algorithm. The new sigmoid equation corrects lower CT estimates while producing nearly identical estimates to the original quadratic equation at higher CT. The demonstrated accuracy of ECTemp™ encourages future research to explore the algorithm’s potential as a non-invasive means of tracking CT circadian rhythms.

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