scholarly journals Towards the Development of Nonlinear Approaches to Discriminate AF from NSR Using a Single-Lead ECG

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 531
Author(s):  
Jieun Lee ◽  
Yugene Guo ◽  
Vasanth Ravikumar ◽  
Elena G. Tolkacheva
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Multiscale Entropy ◽  
Short Term ◽  
Embedding Method ◽  
Ecg Signals ◽  
Normal Sinus ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Insight Into

Paroxysmal atrial fibrillation (Paro. AF) is challenging to identify at the right moment. This disease is often undiagnosed using currently existing methods. Nonlinear analysis is gaining importance due to its capability to provide more insight into complex heart dynamics. The aim of this study is to use several recently developed nonlinear techniques to discriminate persistent AF (Pers. AF) from normal sinus rhythm (NSR), and more importantly, Paro. AF from NSR, using short-term single-lead electrocardiogram (ECG) signals. Specifically, we adapted and modified the time-delayed embedding method to minimize incorrect embedding parameter selection and further support to reconstruct proper phase plots of NSR and AF heart dynamics, from MIT-BIH databases. We also examine information-based methods, such as multiscale entropy (MSE) and kurtosis (Kt) for the same purposes. Our results demonstrate that embedding parameter time delay ( τ ), as well as MSE and Kt values can be successfully used to discriminate between Pers. AF and NSR. Moreover, we demonstrate that τ and Kt can successfully discriminate Paro. AF from NSR. Our results suggest that nonlinear time-delayed embedding method and information-based methods provide robust discriminating features to distinguish both Pers. AF and Paro. AF from NSR, thus offering effective treatment before suffering chaotic Pers. AF.

CHARACTERIZATION OF VENTRICULAR ARRHYTHMIAS USING A SEMANTIC MINING ALGORITHM

2012 ◽  
Vol 12 (03) ◽  
pp. 1250049 ◽  
Author(s):  
MOHD AFZAN OTHMAN ◽  
NORLAILI MAT SAFRI
Keyword(s):  
Ventricular Arrhythmia ◽  
Ventricular Arrhythmias ◽  
Normal Sinus Rhythm ◽  
High Sensitivity ◽  
Real Data ◽  
Biological Behavior ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Semantic Mining ◽  
Electrocardiogram Ecg

Ventricular arrhythmia, especially ventricular fibrillation, is a type of arrhythmia that can cause sudden death. The aim of this paper is to characterize ventricular arrhythmias using semantic mining by extracting their significant characteristics (frequency, damping coefficient and input signal) from electrocardiogram (ECG) signals that represent the biological behavior of the cardiovascular system. Real data from an arrhythmia database are used after noise filtering and were statistically classified into two groups; normal sinus rhythm (N) and ventricular arrhythmia (V). The proposed method achieved high sensitivity and specificity (98.1% and 97.7%, respectively) and was capable of describing the differences between the N and V types in the ECG signal.

scholarly journals Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

2021 ◽  
Vol 17 (02) ◽  
pp. 106
Author(s):  
Mohand Lokman Ahmad Al-dabag ◽  
Haider Th. Salim ALRikabi ◽  
Raid Rafi Omar Al-Nima
Keyword(s):  
Atrial Fibrillation ◽  
Computational Cost ◽  
Extraction Methods ◽  
Support Vector ◽  
Ecg Signal ◽  
Heart Problem ◽  
Ecg Signals ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Low Computational Cost

One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.

scholarly journals Spectral characteristics of ventricular response to atrial fibrillation

1997 ◽  
Vol 273 (6) ◽  
pp. H2811-H2816 ◽  
Author(s):  
Junichiro Hayano ◽  
Fumiyasu Yamasaki ◽  
Seiichiro Sakata ◽  
Akiyoshi Okada ◽  
Seiji Mukai ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Spectral Characteristics ◽  
Short Term ◽  
Spectral Exponent ◽  
Linear Pattern ◽  
Regulatory Processes ◽  
Normal Sinus ◽  
Ventricular Response

To investigate the spectral characteristics of the fluctuation in ventricular response during atrial fibrillation (AF), R-R interval time series obtained from ambulatory electrocardiograms were analyzed in 45 patients with chronic AF and in 30 age-matched healthy subjects with normal sinus rhythm (SR). Although the 24-h R-R interval spectrum during SR showed a 1/ f noise-like downsloping linear pattern when plotted as log power against log frequency, the spectrum during AF showed an angular shape with a breakpoint at a frequency of 0.005 ± 0.002 Hz, by which the spectrum was separated into long-term and short-term components with different spectral characteristics. The short-term component showed a white noise-like flat spectrum with a spectral exponent (absolute value of the regression slope) of 0.05 ± 0.08 and an intercept at 10−2 Hz of 4.9 ± 0.3 log(ms2/Hz). The long-term component had a 1/ f noise-like spectrum with a spectral exponent of 1.26 ± 0.40 and an intercept at 10−4 Hz of 7.0 ± 0.3 log(ms2/Hz), which did not differ significantly from those for the spectrum during SR in the same frequency range [spectral exponent, 1.36 ± 0.06; intercept at 10−4 Hz, 7.1 ± 0.3 log(ms2/Hz)]. The R-R intervals during AF may be a sequence of uncorrelated values over the short term (within several minutes). Over the longer term, however, the R-R interval fluctuation shows the long-range negative correlation suggestive of underlying regulatory processes, and spectral characteristics indistinguishable from those for SR suggest that the long-term fluctuations during AF and SR may originate from similar dynamics of the cardiovascular regulatory systems.

scholarly journals Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pietro Melzi ◽  
Ruben Tolosana ◽  
Alberto Cecconi ◽  
Ancor Sanz-Garcia ◽  
Guillermo J. Ortega ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Area Under The Curve ◽  
Heart Rhythm ◽  
Demographic Groups ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Visualization Techniques ◽  
Feature Visualization

AbstractAtrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72–0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based models.

scholarly journals Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 952 ◽  
Author(s):  
Dae-Young Lee ◽  
Young-Seok Choi
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Coarse Grained ◽  
Multiscale Entropy ◽  
Short Term ◽  
Ecg Signals ◽  
Improved Stability ◽  
Moving Averaging ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series.

scholarly journals DETECTION OF VENTRICULAR FIBRILLATION USING WAVELET TRANSFORM AND PHASE SPACE RECONSTRUCTION FROM ECG SIGNALS

2021 ◽  
pp. 2140036
Author(s):  
SEOK-WOO JANG ◽  
SANG-HONG LEE
Keyword(s):  
Phase Space ◽  
Ventricular Fibrillation ◽  
Wavelet Transforms ◽  
Normal Sinus Rhythm ◽  
Phase Space Reconstruction ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Set Up ◽  
Electrocardiogram Ecg ◽  
External Defibrillator

This study proposes the detection of ventricular fibrillation (VF) by wavelet transforms (WTs) and phase space reconstruction (PSR) from electrocardiogram (ECG) signals. A neural network with weighted fuzzy memberships (NEWFM) is used to detect VF as a classifier. In the first step, the WT was used to remove noise in ECG signals. In the second step, coordinates were mapped from the wavelet coefficients by the PSR. In the final step, NEWFM used the mapped coordinates-based features as inputs. The NEWFM has the bounded sum of weighted fuzzy memberships (BSWFM) that can easily appear the distinctness between the normal sinus rhythm (NSR) and VF in the graphical characteristics. The BSWFM can easily be set up in a portable automatic external defibrillator (AED) to detect VF in an emergency.

scholarly journals TASK-1 current is inhibited by phosphorylation during human and canine chronic atrial fibrillation

2015 ◽  
Vol 308 (2) ◽  
pp. H126-H134 ◽  
Author(s):  
Erin Harleton ◽  
Alessandra Besana ◽  
Parag Chandra ◽  
Peter Danilo ◽  
Tove S. Rosen ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Human Subjects ◽  
Phosphorylation Site ◽  
Chronic Atrial Fibrillation ◽  
Resting Membrane Potential ◽  
Atrial Remodeling ◽  
Protein Levels ◽  
Atrial Tissue ◽  
Normal Sinus

Atrial fibrillation (AF) is a common arrhythmia with significant morbidities and only partially adequate therapeutic options. AF is associated with atrial remodeling processes, including changes in the expression and function of ion channels and signaling pathways. TWIK protein-related acid-sensitive K+ channel (TASK)-1, a two-pore domain K+ channel, has been shown to contribute to action potential repolarization as well as to the maintenance of resting membrane potential in isolated myocytes, and TASK-1 inhibition has been associated with the induction of perioperative AF. However, the role of TASK-1 in chronic AF is unknown. The present study investigated the function, expression, and phosphorylation of TASK-1 in chronic AF in atrial tissue from chronically paced canines and in human subjects. TASK-1 current was present in atrial myocytes isolated from human and canine hearts in normal sinus rhythm but was absent in myocytes from humans with AF and in canines after the induction of AF by chronic tachypacing. The addition of phosphatase to the patch pipette rescued TASK-1 current from myocytes isolated from AF hearts, indicating that the change in current is phosphorylation dependent. Western blot analysis showed that total TASK-1 protein levels either did not change or increased slightly in AF, despite the absence of current. In studies of perioperative AF, we have shown that phosphorylation of TASK-1 at Thr383 inhibits the channel. However, phosphorylation at this site was unchanged in atrial tissue from humans with AF or in canines with chronic pacing-induced AF. We conclude that phosphorylation-dependent inhibition of TASK-1 is associated with AF, but the phosphorylation site responsible for this inhibition remains to be identified.

Sign in / Sign up

Export Citation Format

Share Document