OC11_03 Repeated Forms of Premature Ventricular Complexes and Their Mean Coupling Interval Are Predictors of Imminent Ventricular Tachyarrhythmia: Usefulness of Short Term RR Interval Time Series Obtained From Implantable Cardioverter Defibrillators

Global Heart ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e15
Author(s):  
M. Martinez Alanis ◽  
C. Lerma
Keyword(s):  
Time Series ◽  
Ventricular Tachyarrhythmia ◽  
Implantable Cardioverter Defibrillators ◽  
Short Term ◽  
Interval Time ◽  
Premature Ventricular Complexes ◽  
Rr Interval ◽  
Coupling Interval ◽  
Interval Time Series ◽  
Cardioverter Defibrillators

scholarly journals Anticipation of ventricular tachyarrhythmias by a novel mathematical method: Further insights towards an early warning system in implantable cardioverter defibrillators

2020 ◽  
Author(s):  
Gabriel S. Zamudio ◽  
Manlio F. Márquez ◽  
Marco V. José
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Global Minimum ◽  
Ventricular Tachyarrhythmia ◽  
Regular Graphs ◽  
Implantable Cardioverter Defibrillators ◽  
Data Set ◽  
Interval Time ◽  
Interval Time Series ◽  
Cardioverter Defibrillators

AbstractBackgroundImplantable cardioverter defibrillators (ICD) are the most effective therapy to terminate malignant ventricular arrhythmias (VA) and therefore to prevent sudden cardiac death. Until today, there is no way to predict the onset of such VA. Our aim was to develop a mathematical model that could predict VA in a timely fashion.MethodsWe analyzed the time series of R-R intervals from 3 groups. Two groups from the Spontaneous Ventricular Tachyarrhythmia Database (v 1.0) were analyzed from a set of 81 pairs of R-R interval time series records from patients, each pair containing one record before the VT episode (Dataset 1A) and one control record which was obtained during the follow up visit (Dataset 1B). A third data set was composed of the RR interval time series of 54 subjects without a significant arrhythmia heart disease (Dataset 2). We developed a new method to transform a time series into a network for its analysis, the ε − regular graphs. This novel approach transforms a time series into a network which is sensitive to the quantitative properties of the time series, it has a single parameter (ε) to be adjusted, and it can trace long-range correlations. This procedure allows to use graph theory to extract the dynamics of any time series.ResultsThe average of the difference between the VT and the control record graph degree of each patient, at each time window, reached a global minimum value of −2.12 followed by a drastic increase of the average graph until reaching a local maximum of 5.59. The global minimum and the following local maxima occur at the windows 276 and 393, respectively. This change in the connectivity of the graphs distinguishes two distinct dynamics occurring during the VA, while the states in between the 276 and 393, determine a transitional state. We propose this change in the dynamic of the R-R intervals as a measurable and detectable “early warning” of the VT event, occurring an average of 514.625 seconds (8 : 30 minutes) before the onset of the VT episode.ConclusionsIt is feasible to detect retrospectively early warnings of the VA episode using their corresponding ε − regular graphs, with an average of 8 : 30 minutes before the ICD terminates the VA event.

scholarly journals Does the Temporal Asymmetry of Short-Term Heart Rate Variability Change during Regular Walking? A Pilot Study of Healthy Young Subjects

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xinpei Wang ◽  
Chang Yan ◽  
Bo Shi ◽  
Changchun Liu ◽  
Chandan Karmakar ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Short Term ◽  
Area Index ◽  
Temporal Asymmetry ◽  
Interval Time ◽  
Rr Interval ◽  
Rate Asymmetry ◽  
Interval Time Series

The acceleration and deceleration patterns in heartbeat fluctuations distribute asymmetrically, which is known as heart rate asymmetry (HRA). It is hypothesized that HRA reflects the balancing regulation of the sympathetic and parasympathetic nervous systems. This study was designed to examine whether altered autonomic balance during exercise can lead to HRA changes. Sixteen healthy college students were enrolled, and each student undertook two 5-min ECG measurements: one in a resting seated position and another while walking on a treadmill at a regular speed of 5 km/h. The two measurements were conducted in a randomized order, and a 30-min rest was required between them. RR interval time series were extracted from the 5-min ECG data, and HRA (short-term) was estimated using four established metrics, that is, Porta’s index (PI), Guzik’s index (GI), slope index (SI), and area index (AI), from both raw RR interval time series and the time series after wavelet detrending that removes the low-frequency component of <~0.03 Hz. Our pilot data showed a reduced PI but unchanged GI, SI, and AI during walking compared to resting seated position based on the raw data. Based on the wavelet-detrended data, reduced PI, SI, and AI were observed while GI still showed no significant changes. The reduced PI during walking based on both raw and detrended data which suggests less short-term HRA may underline the belief that vagal tone is withdrawn during low-intensity exercise. GI may not be sensitive to short-term HRA. The reduced SI and AI based on detrended data suggest that they may capture both short- and long-term HRA features and that the expected change in short-term HRA is amplified after removing the trend that is supposed to link to long-term component. Further studies with more subjects and longer measurements are warranted to validate our observations and to examine these additional hypotheses.

scholarly journals Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based Sample Entropy

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 411 ◽  
Author(s):  
Lina Zhao ◽  
Jianqing Li ◽  
Jinle Xiong ◽  
Xueyu Liang ◽  
Chengyu Liu
Keyword(s):  
Time Series ◽  
Physical Meaning ◽  
Sample Entropy ◽  
Ectopic Beat ◽  
Ecg Signal ◽  
Interval Time ◽  
Rr Interval ◽  
Interval Time Series ◽  
Ectopic Beats ◽  
Hrv Analysis

Sample entropy (SampEn) is widely used for electrocardiogram (ECG) signal analysis to quantify the inherent complexity or regularity of RR interval time series (i.e., heart rate variability (HRV)), with the hypothesis that RR interval time series in pathological conditions output lower SampEn values. However, ectopic beats can significantly influence the entropy values, resulting in difficulty in distinguishing the pathological situation from normal situations. Although a theoretical operation is to exclude the ectopic intervals during HRV analysis, it is not easy to identify all of them in practice, especially for the dynamic ECG signal. Thus, it is important to suppress the influence of ectopic beats on entropy results, i.e., to improve the robustness and stability of entropy measurement for ectopic beats-inserted RR interval time series. In this study, we introduced a physical threshold-based SampEn method, and tested its ability to suppress the influence of ectopic beats for HRV analysis. An experiment on the PhysioNet/MIT RR Interval Databases showed that the SampEn use physical meaning threshold has better performance not only for different data types (normal sinus rhythm (NSR) or congestive heart failure (CHF) recordings), but also for different types of ectopic beat (atrial beats, ventricular beats or both), indicating that using a physical meaning threshold makes SampEn become more consistent and stable.

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