scholarly journals Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7122
Author(s):  
Alessio Rossi ◽  
Dino Pedreschi ◽  
David A. Clifton ◽  
Davide Morelli
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Missing Values ◽  
Mean Squared Error ◽  
Time Window ◽  
Small Error ◽  
Motion Artifacts ◽  
Window Length ◽  
Short Time Window ◽  
Short Time

Application of ultra–short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people’s daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.

scholarly journals Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3235
Author(s):  
Koichi Fujiwara ◽  
Shota Miyatani ◽  
Asuka Goda ◽  
Miho Miyajima ◽  
Tetsuo Sasano ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mean Squared Error ◽  
False Positive Rate ◽  
Premature Ventricular Contraction ◽  
Heart Rate Variability Analysis ◽  
Unified Framework ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Positive Rate

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

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.

scholarly journals 231 A SHORT TIME WINDOW TO PROFIT FROM IL-4 PLUS IL-10 ADDITION TO PROTECT CARTILAGE FROM BLOOD-INDUCED DAMAGE IN VITRO

2011 ◽  
Vol 19 ◽  
pp. S113
Author(s):  
M.E. van Meegeren ◽  
N.W. Jansen ◽  
G. Roosendaal ◽  
S.C. Mastbergen ◽  
F.P. Lafeber
Keyword(s):  
Time Window ◽  
Short Time Window ◽  
Short Time

scholarly journals 383. Feasibility of Specimen Self-collection in Young Children Undergoing SARS-CoV-2 Surveillance for In-person Learning

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S293-S293
Author(s):  
Jonathan Altamirano ◽  
Grace Tam ◽  
Marcela Lopez ◽  
India Robinson ◽  
Leanne Chun ◽  
...  
Keyword(s):  
Young Children ◽  
Time Window ◽  
School Administrators ◽  
Error Rates ◽  
Independent Observer ◽  
Specimen Collection ◽  
Timing Data ◽  
Short Time Window ◽  
Short Time ◽  
Over Time

Abstract Background While pediatric cases of COVID-19 are at low risk for adverse events, schoolchildren should be considered for surveillance as they can become infected at school and serve as sources of household or community transmission. Our team assessed the feasibility of young children self-collecting SARS-CoV-2 samples for surveillance testing in an educational setting. Methods Students at a K-8 school were tested weekly for SARS-CoV-2 from September 2020 - June 2021. Error rates were collected from September 2020 - January 2021. Clinical staff provided all students with instructions for anterior nares specimen self-collection and then observed them to ensure proper technique. Instructions included holding the sterile swab while making sure not to touch the tip, inserting the swab into their nostril until they start to feel resistance, and rubbing the swab in four circles before repeating the process in their other nostril. An independent observer timed random sample self-collections from April - June 2021. Results 2,590 samples were collected from 209 students during the study period when data on error rates were collected. Errors occurred in 3.3% of all student encounters (n=87). Error rates over time are shown in Figure 1, with the highest rate occurring on the first day of testing (n=20/197, 10.2%) and the lowest in January 2021 (n=1/202, 0.5%). 2,574 visits for sample self-collection occurred during the study period when independent timing data was collected (April - June 2021). Of those visits, 7.5% (n=193) were timed. The average duration of each visit was 70 seconds. Figure 1. Swab Error Rates Over Time Conclusion Pediatric self-collected lower nasal swabs are a viable and easily tolerated specimen collection method for SARS-CoV-2 surveillance in school settings, as evidenced by the low error rate and short time window of sample self-collection during testing. School administrators should expect errors to drop quickly after implementing testing. Disclosures All Authors: No reported disclosures

Dual origin of the floor plate in the avian embryo

Development ◽  
2002 ◽  
Vol 129 (20) ◽  
pp. 4785-4796 ◽  
Author(s):  
Jean-Baptiste Charrier ◽  
Françoise Lapointe ◽  
Nicole M. Le Douarin ◽  
Marie-Aimée Teillet
Keyword(s):  
Time Window ◽  
Gene Expression Pattern ◽  
Floor Plate ◽  
Avian Embryo ◽  
Plate Formation ◽  
Unknown Factor ◽  
Short Time Window ◽  
Neural Ectoderm ◽  
Dual Origin ◽  
Short Time

Molecular analysis carried out on quail-chick chimeras, in which quail Hensen’s node was substituted for its chick counterpart at the five- to six-somite stage (ss), showed that the floor plate of the avian neural tube is composed of distinct areas: (1) a median one (medial floor plate or MFP) derived from Hensen’s node and characterised by the same gene expression pattern as the node cells (i.e. expression of HNF3β and Shh to the exclusion of genes early expressed in the neural ectoderm such as CSox1); and (2) lateral regions that are differentiated from the neuralised ectoderm (CSox1 positive) and form the lateral floor plate (LFP). LFP cells are induced by the MFP to express HNF3β transiently, Shh continuously and other floor-plate characteristic genes such as Netrin. In contrast to MFP cells, LFP cells also express neural markers such as Nkx2.2 and Sim1. This pattern of avian floor-plate development presents some similarities to floor-plate formation in zebrafish embryos. We also demonstrate that, although MFP and LFP have different embryonic origins in normal development, one can experimentally obtain a complete floor plate in the neural epithelium by the inductive action of either a notochord or a MFP. The competence of the neuroepithelium to respond to notochord or MFP signals is restricted to a short time window, as only the posterior-most region of the neural plate of embryos younger than 15 ss is able to differentiate a complete floor plate comprising MFP and LFP. Moreover, MFP differentiation requires between 4 and 5 days of exposure to the inducing tissues. Under the same conditions LFP and SHH-producing cells only induce LFP-type cells. These results show that the capacity to induce a complete floor plate is restricted to node-derived tissues and probably involves a still unknown factor that is not SHH, the latter being able to induce only LFP characteristics in neuralised epithelium.

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