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BMJ Open Diabetes Research & Care Dec 2022Adipose insulin-resistant but normal weight phenotype has not been reported and hence was characterized in young Japanese women.
INTRODUCTION
Adipose insulin-resistant but normal weight phenotype has not been reported and hence was characterized in young Japanese women.
RESEARCH DESIGN AND METHODS
Body composition, a broad range of cardiometabolic health and dietary intake were cross-sectionally measured in 166 normal weight young Japanese women. They were grouped into tertile of adipose tissue-insulin resistance (AT-IR) index (fasting insulin×free fatty acids) and analyzed by analysis of variance and then Bonferroni's multiple comparison procedure.
RESULTS
Body mass index averaged <21 kg/m and waist <72 cm, and did not differ among three groups of women. Fasting glucose and triglycerides and homeostasis model assessment-insulin resistance were higher in the highest compared with the median and lowest AT-IR tertile. However, there was no difference in fat mass and distribution, high-density lipoprotein cholesterol and blood pressure. In addition, high-sensitivity C reactive protein (hsCRP) and resting pulse rate were higher as well. In multivariate logistic regression analyses, fasting glucose (OR: 1.10, 95% CI: 1.02 to 1.18, p=0.012), fasting triglycerides (OR: 1.04, 95% CI: 1.02 to 1.06, p<0.001), resting pulse rate (OR: 1.07, 95% CI: 1.03 to 1.11, p<0.001) and hsCRP (OR: 2.30, 95% CI: 1.01 to 5.2, p=0.04) were associated with the high AT-IR tertile.
CONCLUSIONS
Adipose insulin-resistant but normal weight phenotype may be associated with increased sympathetic nervous system and low-grade systemic inflammation in addition to glucose and lipid dysmetabolism through mechanisms unrelated to adiposity in young Japanese women.
Topics: Humans; Female; Insulin; C-Reactive Protein; Adiposity; Triglycerides; Insulin Resistance; Heart Rate; East Asian People; Blood Glucose; Obesity; Insulin, Regular, Human; Fasting
PubMed: 36593657
DOI: 10.1136/bmjdrc-2022-003013 -
Computational and Mathematical Methods... 2021Pulse rate variability monitoring and atrial fibrillation detection algorithms have been widely used in wearable devices, but the accuracies of these algorithms are...
BACKGROUND
Pulse rate variability monitoring and atrial fibrillation detection algorithms have been widely used in wearable devices, but the accuracies of these algorithms are restricted by the signal quality of pulse wave. Time synchronous averaging is a powerful noise reduction method for periodic and approximately periodic signals. It is usually used to extract single-period pulse waveforms, but has nothing to do with pulse rate variability monitoring and atrial fibrillation detection traditionally. If this method is improved properly, it may provide a new way to measure pulse rate variability and to detect atrial fibrillation, which may have some potential advantages under the condition of poor signal quality.
OBJECTIVE
The objective of this paper was to develop a new measure of pulse rate variability by improving existing time synchronous averaging and to detect atrial fibrillation by the new measure of pulse rate variability.
METHODS
During time synchronous averaging, two adjacent periods were regarded as the basic unit to calculate the average signal, and the difference between waveforms of the two adjacent periods was the new measure of pulse rate variability. 3 types of distance measures (Euclidean distance, Manhattan distance, and cosine distance) were tested to measure this difference on a simulated training set with a capacity of 1000. The distance measure, which can accurately distinguish regular pulse rate and irregular pulse rate, was used to detect atrial fibrillation on the testing set with a capacity of 62 (11 with atrial fibrillation, 8 with premature contraction, and 43 with sinus rhythm). The receiver operating characteristic curve was used to evaluate the performance of the indexes.
RESULTS
The Euclidean distance between waveforms of the two adjacent periods performs best on the training set. On the testing set, the Euclidean distance in atrial fibrillation group is significantly higher than that of the other two groups. The area under receiver operating characteristic curve to identify atrial fibrillation was 0.998. With the threshold of 2.1, the accuracy, sensitivity, and specificity were 98.39%, 100%, and 98.04%, respectively. This new index can detect atrial fibrillation from pulse wave signal.
CONCLUSION
This algorithm not only provides a new perspective to detect AF but also accomplishes the monitoring of PRV and the extraction of single-period pulse wave through the same technical route, which may promote the popularization and application of pulse wave.
Topics: Algorithms; Analysis of Variance; Atrial Fibrillation; Computational Biology; Diagnosis, Computer-Assisted; Heart Rate; Humans; Machine Learning; Pulse Wave Analysis; ROC Curve; Radial Artery; Wearable Electronic Devices
PubMed: 33868451
DOI: 10.1155/2021/5597559 -
PloS One 2022Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely...
BACKGROUND
Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely used as a substitute of heart rate variability (HRV). The method is used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applications. However, PPG is highly susceptible to motion artifacts and environmental noise. A validation study is required to investigate the accuracy of PPG-based wearable devices in free-living conditions.
OBJECTIVE
We evaluate the accuracy of PPG signals-collected by the Samsung Gear Sport smartwatch in free-living conditions-in terms of HR and time-domain and frequency-domain HRV parameters against a medical-grade chest electrocardiogram (ECG) monitor.
METHODS
We conducted 24-hours monitoring using a Samsung Gear Sport smartwatch and a Shimmer3 ECG device. The monitoring included 28 participants (14 male and 14 female), where they engaged in their daily routines. We evaluated HR and HRV parameters during the sleep and awake time. The parameters extracted from the smartwatch were compared against the ECG reference. For the comparison, we employed the Pearson correlation coefficient, Bland-Altman plot, and linear regression methods.
RESULTS
We found a significantly high positive correlation between the smartwatch's and Shimmer ECG's HR, time-domain HRV, LF, and HF and a significant moderate positive correlation between the smartwatch's and shimmer ECG's LF/HF during sleep time. The mean biases of HR, time-domain HRV, and LF/HF were low, while the biases of LF and HF were moderate during sleep. The regression analysis showed low error variances of HR, AVNN, and pNN50, moderate error variances of SDNN, RMSSD, LF, and HF, and high error variances of LF/HF during sleep. During the awake time, there was a significantly high positive correlation of AVNN and a moderate positive correlation of HR, while the other parameters indicated significantly low positive correlations. RMSSD and SDNN showed low mean biases, and the other parameters had moderate mean biases. In addition, AVNN had moderate error variance while the other parameters indicated high error variances.
CONCLUSION
The Samsung smartwatch provides acceptable HR, time-domain HRV, LF, and HF parameters during sleep time. In contrast, during the awake time, AVNN and HR show satisfactory accuracy, and the other HRV parameters have high errors.
Topics: Female; Male; Humans; Heart Rate; Correlation of Data; Exercise
PubMed: 36480505
DOI: 10.1371/journal.pone.0268361 -
Sensors (Basel, Switzerland) Sep 2021Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is...
Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual's autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0.
Topics: Algorithms; Autonomic Nervous System; Fingers; Heart Rate; Photoplethysmography; Pulse; Signal Processing, Computer-Assisted
PubMed: 34577448
DOI: 10.3390/s21186241 -
Sensors (Basel, Switzerland) May 2021Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV...
Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP's PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP and ECG over 15 opportunities. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP's proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10-11%) and SWC (5-5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP's proprietary filter, which approached or exceeded the CV (3-13%) and SWC (1.5-6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.
Topics: Electrocardiography; Heart Rate; Photoplethysmography; Wrist; Wrist Joint
PubMed: 34065516
DOI: 10.3390/s21103571 -
Sensors (Basel, Switzerland) Dec 2021Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR)...
Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.
Topics: Algorithms; Heart Rate; Monitoring, Physiologic; Radar; Respiration; Respiratory Rate; Signal Processing, Computer-Assisted; Vital Signs
PubMed: 35009628
DOI: 10.3390/s22010083 -
Frontiers in Public Health 2023Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement...
Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement and monitoring typically relies on self-reporting. However, wearable smart technologies offer a novel strategy to continuously and non-invasively collect objective health data in the real-world. A novel electrocardiogram (ECG) feature has recently been introduced to the Apple Watch device. Interestingly, ECG data can be used to derive Heart Rate Variability (HRV) features commonly used in the identification of stress, suggesting that the Apple Watch ECG app could potentially be utilized as a simple, cost-effective, and minimally invasive tool to monitor individual stress levels. Here we collected ECG data using the Apple Watch from 36 health participants during their daily routines. Heart rate variability (HRV) features from the ECG were extracted and analyzed against self-reported stress questionnaires based on the DASS-21 questionnaire and a single-item LIKERT-type scale. Repeated measures ANOVA tests did not find any statistical significance. Spearman correlation found very weak correlations ( < 0.05) between several HRV features and each questionnaire. The results indicate that the Apple Watch ECG cannot be used for quantifying stress with traditional statistical methods, although future directions of research (e.g., use of additional parameters and Machine Learning) could potentially improve stress quantification with the device.
Topics: Humans; Heart Rate; Electrocardiography; Wearable Electronic Devices; Cardiovascular Diseases; Hypertension
PubMed: 37475772
DOI: 10.3389/fpubh.2023.1178491 -
Scientific Reports Apr 2022This study presents findings in the terahertz (THz) frequency spectrum for non-contact cardiac sensing applications. Cardiac pulse information is simultaneously...
This study presents findings in the terahertz (THz) frequency spectrum for non-contact cardiac sensing applications. Cardiac pulse information is simultaneously extracted using THz waves based on the established principles in electronics and optics. The first fundamental principle is micro-Doppler motion effect. This motion based method, primarily using coherent phase information from the radar receiver, has been widely exploited in microwave frequency bands and has recently found popularity in millimeter waves (mmWave) for breathe rate and heart rate detection. The second fundamental principle is reflectance based optical measurement using infrared or visible light. The variation in the light reflection is proportional to the volumetric change of the heart, often referred as photoplethysmography (PPG). Herein, we introduce the concept of terahertz-wave-plethysmography (TPG), which detects blood volume changes in the upper dermis tissue layer by measuring the reflectance of THz waves, similar to the existing remote PPG (rPPG) principle. The TPG principle is justified by scientific deduction, electromagnetic wave simulations and carefully designed experimental demonstrations. Additionally, pulse measurements from various peripheral body parts of interest (BOI), palm, inner elbow, temple, fingertip and forehead, are demonstrated using a wideband THz sensing system developed by the Terahertz Electronics Lab at Arizona State University, Tempe. Among the BOIs under test, it is found that the measurements from forehead BOI gives the best accuracy with mean heart rate (HR) estimation error 1.51 beats per minute (BPM) and standard deviation 1.08 BPM. The results validate the feasibility of TPG for direct pulse monitoring. A comparative study on pulse sensitivity is conducted between TPG and rPPG. The results indicate that the TPG contains more pulsatile information from the forehead BOI than that in the rPPG signals in regular office lighting condition and thus generate better heart rate estimation statistic in the form of empirical cumulative distribution function of HR estimation error. Last but not least, TPG penetrability test for covered skin is demonstrated using two types of garment materials commonly used in daily life.
Topics: Heart Rate; Humans; Photoplethysmography; Plethysmography; Pulse; Radar
PubMed: 35428772
DOI: 10.1038/s41598-022-09801-w -
The Journal of the Acoustical Society... Jun 2023Users of cochlear implants (CIs) struggle in situations that require selective hearing to focus on a target source while ignoring other sources. One major reason for...
Users of cochlear implants (CIs) struggle in situations that require selective hearing to focus on a target source while ignoring other sources. One major reason for that is the limited access to timing cues such as temporal pitch or interaural time differences (ITDs). Various approaches to improve timing-cue sensitivity while maintaining speech understanding have been proposed, among them inserting extra pulses with short inter-pulse intervals (SIPIs) into amplitude-modulated (AM) high-rate pulse trains. Indeed, SIPI rates matching the naturally occurring AM rates improve pitch discrimination. For ITD, however, low SIPI rates are required, potentially mismatching the naturally occurring AM rates and thus creating unknown pitch effects. In this study, we investigated the perceptual contribution of AM and SIPI rate to pitch discrimination in five CI listeners and with two AM depths (0.1 and 0.5). Our results show that the SIPI-rate cue generally dominated the percept for both consistent and inconsistent cues. When tested with inconsistent cues, also the AM rate contributed, however, at the large AM depth only. These findings have implications when aiming at jointly improving temporal-pitch and ITD sensitivity in a future mixed-rate stimulation approach.
Topics: Cues; Heart Rate; Pitch Discrimination; Hearing; Cochlear Implantation
PubMed: 37307025
DOI: 10.1121/10.0019452 -
Sensors (Basel, Switzerland) Jan 2023Despite the notable recent developments in the field of remote photoplethysmography (rPPG), extracting a reliable pulse rate variability (PRV) signal still remains a...
Despite the notable recent developments in the field of remote photoplethysmography (rPPG), extracting a reliable pulse rate variability (PRV) signal still remains a challenge. In this study, eight image-based photoplethysmography (iPPG) extraction methods (GRD, AGRD, PCA, ICA, LE, SPE, CHROM, and POS) were compared in terms of pulse rate (PR) and PRV features. The algorithms were made robust for motion and illumination artifacts by using ad hoc pre- and postprocessing steps. Then, they were systematically tested on the public dataset UBFC-RPPG, containing data from 42 subjects sitting in front of a webcam (30 fps) while playing a time-sensitive mathematical game. The performances of the algorithms were evaluated by statistically comparing iPPG-based and finger-PPG-based PR and PRV features in terms of Spearman's correlation coefficient, normalized root mean square error (NRMSE), and Bland-Altman analysis. The study revealed POS and CHROM techniques to be the most robust for PR estimation and the assessment of overall autonomic nervous system (ANS) dynamics by using PRV features in time and frequency domains. Furthermore, we demonstrated that a reliable characterization of the vagal tone is made possible by computing the Poincaré map of PRV series derived from the POS and CHROM methods. This study supports the use of iPPG systems as promising tools to obtain clinically useful and specific information about ANS dynamics.
Topics: Humans; Photoplethysmography; Signal Processing, Computer-Assisted; Heart Rate; Diagnostic Imaging; Algorithms; Wearable Electronic Devices
PubMed: 36772543
DOI: 10.3390/s23031505