scholarly journals Classification of cardiovascular time series based on different coupling structures using recurrence networks analysis

2013 ◽  
Vol 371 (1997) ◽  
pp. 20110623 ◽  
Author(s):  
Gonzalo Marcelo Ramírez Ávila ◽  
Andrej Gapelyuk ◽  
Norbert Marwan ◽  
Thomas Walther ◽  
Holger Stepan ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Time Series ◽  
Path Length ◽  
Clustering Coefficient ◽  
Early Prediction ◽  
Quadratic Discriminant Analysis ◽  
Novel Approach ◽  
Networks Analysis

We analyse cardiovascular time series with the aim of performing early prediction of preeclampsia (PE), a pregnancy-specific disorder causing maternal and foetal morbidity and mortality. The analysis is made using a novel approach, namely the ε -recurrence networks applied to a phase space constructed by means of the time series of the variabilities of the heart rate and the blood pressure (systolic and diastolic). All the possible coupling structures among these variables are considered for the analysis. Network measures such as average path length, mean coreness, global clustering coefficient and scale-local transitivity dimension are computed and constitute the parameters for the subsequent quadratic discriminant analysis. This allows us to predict PE with a sensitivity of 91.7 per cent and a specificity of 68.1 per cent, thus validating the use of this method for classifying healthy and preeclamptic patients.

scholarly journals Patterns of Circadian Variation in 24-Hour Ambulatory Blood Pressure, Heart Rate, and Sympathetic Tone Correlate with Cardiovascular Disease Risk: A Cluster Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Myung Han Hyun ◽  
Jun Hyuk Kang ◽  
Sunghwan Kim ◽  
Jin Oh. Na ◽  
Cheol Ung Choi ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Time Series ◽  
Circadian Variation ◽  
Sympathetic Tone ◽  
Atherosclerotic Cardiovascular Disease ◽  
Metabolic Factors ◽  
Multiple Variables ◽  
Ascvd Risk

To investigate whether specific time series patterns for blood pressure (BP), heart rate (HR), and sympathetic tone are associated with metabolic factors and the 10-year risk of atherosclerotic cardiovascular disease (ASCVD). A total of 989 patients who underwent simultaneous 24-hour ambulatory BP and Holter electrocardiogram monitoring were enrolled. The patients were categorized into sixteen groups according to their circadian patterns using the consensus clustering analysis method. Metabolic factors, including cholesterol profiles and apolipoprotein, were compared. The 10-year ASCVD risk was estimated based on the Framingham risk model. Overall, 16 significant associations were found between the clinical variables and cluster groups. Age was commonly associated with all clusters in systolic BP (SBP), diastolic BP (DBP), HR, and sympathetic tone. Metabolic indicators, including diabetes, body mass index, total cholesterol, high-density lipoprotein, and apolipoprotein, were associated with the four sympathetic tone clusters. In the crude analysis, the ASCVD risk increased incrementally from clusters 1 to 4 across SBP, DBP, HR, and sympathetic tone. After adjustment for multiple variables, however, only sympathetic tone clusters 3 and 4 showed a significantly high proportion of patients at high risk (≥7.5%) of 10-year ASCVD (odds ratio OR=5.90, 95% confidential interval CI=1.27–27.46, and P value = 0.024 and OR=15.28, 95% CI=3.59–65.11, and P value < 0.001, respectively). Time series patterns of BP, HR, and sympathetic tone can serve as an indicator of aging. Circadian variations in sympathetic tone can provide prognostic information about patient metabolic profiles and indicate future ASCVD risk.

scholarly journals Classification of Literary Works: Fractality and Complexity of the Narrative, Essay, and Research Article

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 904
Author(s):  
Aldo Ramirez-Arellano
Keyword(s):  
Complex Network ◽  
Path Length ◽  
Research Topic ◽  
Clustering Coefficient ◽  
Node Degree ◽  
Original Text ◽  
Literary Works ◽  
Research Article ◽  
A Current

A complex network as an abstraction of a language system has attracted much attention during the last decade. Linguistic typological research using quantitative measures is a current research topic based on the complex network approach. This research aims at showing the node degree, betweenness, shortest path length, clustering coefficient, and nearest neighbourhoods’ degree, as well as more complex measures such as: the fractal dimension, the complexity of a given network, the Area Under Box-covering, and the Area Under the Robustness Curve. The literary works of Mexican writers were classify according to their genre. Precisely 87% of the full word co-occurrence networks were classified as a fractal. Also, empirical evidence is presented that supports the conjecture that lemmatisation of the original text is a renormalisation process of the networks that preserve their fractal property and reveal stylistic attributes by genre.

scholarly journals Effects of autonomic blockade on self-similarity of blood pressure and heart rate time series

2005 ◽  
Author(s):  
P. Castiglioni ◽  
L. Quintin ◽  
A. Cividjan ◽  
G. Parati ◽  
M. Di Rienzo
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Time Series ◽  
Self Similarity ◽  
Autonomic Blockade

Effect of Head-Down Tilt on Cardiovascular Control in Healthy Subjects: A Spectral Analytic Approach

1995 ◽  
Vol 88 (1) ◽  
pp. 87-93 ◽  
Author(s):  
F. Weise ◽  
G. M. London ◽  
A. P. Guerin ◽  
B. M. Pannier ◽  
J.-L. Elghozi
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Time Series ◽  
Systolic Blood Pressure ◽  
Vascular Resistance ◽  
High Frequency ◽  
Area Under The Curve ◽  
Low Frequency ◽  
Frequency Component ◽  
Forearm Vascular Resistance

1. The purpose of this investigation was to determine non-invasively the changes in autonomic cardiovascular control observed in normal subjects submitted to acute cardiopulmonary blood volume expansion by 100° head-down tilt. The effect of head-down tilt on finger blood pressure and heart rate fluctuations was studied by means of power spectral analysis in 12 healthy men. 2. Amplitude spectra of heart rate and blood pressure rhythmicity were estimated at the low-frequency (60–140 mHz, 10-s rhythm) and high-frequency (area under the curve at mean respiration rate ± 50 mHz) component. Transfer gain and phase were calculated between systolic blood pressure and heart rate. Forearm vascular resistance was estimated to validate the head-down procedure. 3. Forearm vascular resistance decreased significantly from 19.82 (16.34–26.46) mmHg ml−1 min 100 ml to 18.05 (13.69–22.88) mmHg ml−1 min 100 ml (P < 0.01) during head-down tilt (values are medians and 25 and 75 percentiles). The overall variability (total area under the curve of the spectrum from 20 to 500 mHz) of blood pressure and heart rate time series was consistently reduced with head-down tilt. 4. The spectral pattern of systolic blood pressure showed a diminution of the absolute and relative low-frequency component during head-down tilt: absolute log-transformed values, 2.86 (2.80–2.94) mmHg/Hz1/2 versus 2.77 (2.72–2.82) mmHg/Hz1/2 (P < 0.05); relative values, 35% (32–37%) versus 32% (29–32%) (P < 0.05). In heart rate spectra only the absolute low-frequency component decreased. There was no change in the high-frequency component in all time series or in the transfer gain and phase during head-down tilt. 5. It is concluded that head-down tilt is a simple manoeuvre to diminish the 10-s rhythm in systolic blood pressure, which may reflect the reduced sympathetic vasomotor control after cardiopulmonary baroreceptor loading.

scholarly journals A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 121
Author(s):  
Sichen Li ◽  
Mélissa Zacharias ◽  
Jochem Snuverink ◽  
Jaime Coello de Portugal ◽  
Fernando Perez-Cruz ◽  
...  
Keyword(s):  
Time Series ◽  
Binary Classification ◽  
Multivariate Time Series ◽  
Proton Accelerator ◽  
Particle Accelerators ◽  
Recurrence Plots ◽  
Time Loss ◽  
Safety Measures ◽  
Novel Approach

The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71±0.01 compared to 0.65±0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5±0.2 s per interlock.

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