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.

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 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.

Ventricular Tachycardia and Ventricular Fibrillation Analysis Based on Jensen-Shannon Divergence Analysis

2013 ◽  
Vol 321-324 ◽  
pp. 712-715
Author(s):  
Zheng Zhong Zheng ◽  
Jun Chang Zhao ◽  
Jun Wang
Keyword(s):  
Ventricular Tachycardia ◽  
Ventricular Fibrillation ◽  
Sinus Rhythm ◽  
Complexity Analysis ◽  
Heart Function ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Practice Of Medicine ◽  
Cardiac Sudden Death ◽  
Jensen Shannon Divergence

t is an important method for using electrocardiogram (ECG) to detect and diagnose heart function in clinical practice of medicine. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are serious threats for peoples lives, they often bring about cardiac sudden death. In this paper, the complexity analysis method based on Jensen-Shannon Divergence was used to calculate the complexity of the normal sinus rhythm signals, VT, VF. The study found that the VF was highest, followed by that of VT, and that of normal sinus rhythm signals was minimum. The result can be used to assisted clinical diagnosis.

scholarly journals Classification of cardiac arrhythmias using deep learning

2018 ◽  
Vol 7 (3.3) ◽  
pp. 401
Author(s):  
Jeong Hwan Kim ◽  
Jeong Whan Lee ◽  
Kyeong Seop Kim
Keyword(s):  
Deep Learning ◽  
Normal Sinus Rhythm ◽  
Premature Ventricular Contraction ◽  
Heart Rhythm ◽  
Future Research ◽  
Normal Sinus ◽  
Input Layer ◽  
Hidden Layer ◽  
Electrocardiogram Ecg

Background/Objectives: The main objective of this research is to design Deep Learning (DL) architecture to classify an electrocardiogram (ECG) signal into normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC) or right/left bundle branch block (RBBB/LBBB) arrhythmia by empirically optimizing the numbers of hidden layers, the number of neurons in each hidden layer and the number of neurons in input layer in DL model.Methods/Statistical analysis: For our experimental simulations, PhysioBank-MIT/BIH annotated ECG database was considered to classify heart beats into abnormal rhythms (PVC, APC, RBBB, LBBB) or normal sinus. The performance of classifying ECG beats by the proposed DL architecture was evaluated by computing the overall accuracy of classifying NSR or four different arrhythmias.Findings: Base on testing MIT/BIH arrhythmia database, the proposed DL model can classify the heart rhythm into one of NSR, PVC, APC, RBBB or LBBB beat with the mean accuracy of 95.5% by implementing DL architecture with 200 neurons in input layer, 100 neurons in the first and second hidden layer, respectively and 80 neurons in the 3rd hidden layer.Improvements/Applications: Our experimental results show that the proposed DL model might not be quite accurate for detecting APC beats due to its morphological resemblance of NSR. Therefore, we might need to design more sophisticated DL architecture by including more temporal characteristics of APC to increase the classification accuracy of APC arrhythmia in the future research efforts. 

Nature of [Ca2+]i transients during ventricular fibrillation and quinidine treatment in perfused rat hearts

1994 ◽  
Vol 266 (4) ◽  
pp. H1473-H1484
Author(s):  
S. Kojima ◽  
J. Wikman-Coffelt ◽  
S. T. Wu ◽  
W. W. Parmley
Keyword(s):  
Ventricular Fibrillation ◽  
Control Level ◽  
Left Ventricular Pressure ◽  
Left Ventricular ◽  
Acetoxymethyl Ester ◽  
Ecg Signals ◽  
Rat Hearts ◽  
Perfused Rat Hearts ◽  
Electrocardiogram Ecg ◽  
The Relationship

We studied intracellular Ca2+ concentration ([Ca2+]i) and the electrocardiographic signals during pacing-induced ventricular fibrillation (VF) and quinidine treatment (0.4 mg/min) using surface fluorometry in indo 1-acetoxymethyl ester (AM)-loaded perfused rat hearts. [Ca2+]i was evaluated as the indo 1 fluorescence ratio (F400/F510) and expressed as a percentage of the control amplitude of F400/F510 transients. F400/F510 increased to approximately 250% during 2- (n = 14) or 20-min (n = 9) VF. Quinidine significantly decreased F400/F510 by 60% after 2-min VF; however, this effect was blunted after 20-min VF. After 2-min VF, F400/F510 and left ventricular pressure recovered almost to the control level. However, recovery of F400/F510 and ventricular function was poor after 20-min VF. The relationship between [Ca2+]i and the electrocardiogram (ECG) during VF was evaluated by power spectrum analysis of F400/F510 and ECG signals. During VF (25 +/- 3 Hz) with high irregularity, there were no clear [Ca2+]i transients (n = 110). When the cardiac rhythm (22 +/- 3 Hz) was regular, including ventricular tachycardia, there were recognizable [Ca2+]i signals with dominant frequencies that were the same (n = 2), one-half (n = 12), or one-third (n = 1) of the ECG frequencies. The highest frequency of the [Ca2+]i transients was 19 Hz. During quinidine treatment, the VF rate decreased significantly, and clear [Ca2+]i transients were noted in all records responding to every one or two ECG signals. The conclusions were the following: 1) [Ca2+]i responds to electrical signals rapidly (up to 19 Hz) during VF. This fast [Ca2+]i response is a probable cause of high [Ca2+]i during VF. 2) Quinidine decreased [Ca2+]i after 2-min VF possibly in part by slowing the VF and [Ca2+]i transients rates. 3) 20-min VF caused [Ca2+]i overload and poor functional recovery after defibrillation.

scholarly journals ECG Classification using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 2492-2494
Keyword(s):  
Heart Failure ◽  
Cardiovascular Disease ◽  
Congestive Heart Failure ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Original Data ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Electrocardiogram Signals

Recently, the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a important part in the clinical diagnosis of cardiovascular disease. Convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features using wavelet .The classification of ARR (Arrhythmia), CHF (Congestive Heart Failure), and NSR (Normal Sinus Rhythm) signals. The experimental results contains on ARR signals from the MIT-BIH arrhythmia,CHF signals from the BIDMC Congestive Heart Failure and NSR signals from the MIT-BIH Normal Sinus Rhythm Databases show that the proposed method achieves a promising classification accuracy of 90.63%, significantly outperforming several typical ECG classification methods.

scholarly journals Research on Algorithm of Extracting PPG Signal for Detecting Atrial Fibrillation based on Probability Density Function

2015 ◽  
Vol 9 (1) ◽  
pp. 179-184 ◽  
Author(s):  
Kang Liang ◽  
Ying Sun ◽  
Fuying Tian ◽  
Shenghua Ye
Keyword(s):  
Atrial Fibrillation ◽  
Phase Space ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Normal Sinus Rhythm ◽  
New Method ◽  
Diagram Method ◽  
Normal Sinus ◽  
Probability Density Function Pdf

The paper introduced a new method based on probability density function (PDF) and phase space diagram method for photoplethysmography (PPG) signal extracting. In the paper, PPG information was generated from human fingertips by smartphones. The pulse wave period was then separated and reconstructed into probability density function (PDF) by the phase space diagram algorithm. The difference between normal sinus rhythm (NSR) and atrial fibrillation (AF) was finally found by skewness of the PDF. The results of the present study demonstrates that the new method is vividly viable for detecting AF on the smartphone.

AN EFFICIENT ALGORITHM FOR R PEAKS DETECTION OF ELECTROCARDIOGRAM SIGNALS

2021 ◽  
Vol 21 (07) ◽  
Author(s):  
SHUJUAN WANG ◽  
JUNFEN CHENG ◽  
FANCHUANG LI ◽  
YANZHONG WANG ◽  
WANG LIU ◽  
...  
Keyword(s):  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Decision Rules ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Band Pass Filter ◽  
Accurate Analysis ◽  
Pass Filter ◽  
Ecg Signals ◽  
Normal Sinus

Efficient [Formula: see text] peaks detection is the key to the accurate analysis of electrocardiogram (ECG) signals which is a benefit to the early detection of cardiovascular diseases. In recent years, many effective [Formula: see text] peaks detection methods have been proposed, however, the false detection rate is relatively high when the noisy ECG signal is involved. Based on the property of MTEO that it could enhance the features of signal, a novel [Formula: see text] peaks detection algorithm is proposed in this paper to deal with ECG signals with low SNR. The algorithm includes two stages. In the first stage, a band-pass filter is used for eliminating noise, then the first-order forward differentiation and MTEO are used to transform the ECG signals, at last, the output of MTEO is smoothed with a Moving Averaging filter. In the second stage, the adaptive thresholds method and efficient decision rules are applied to detect the true [Formula: see text] peaks. The efficiency and robustness of the proposed method are substantiated on MIT-BIH Arrhythmia Database (MITDB), Fantasia Database and MIT-BIH Normal Sinus Rhythm Database. The testing of the proposed method on the MITDB showed the following results: Sensitivity [Formula: see text], Positive predictivity [Formula: see text] and Accuracy [Formula: see text]. On Fantasia Database involvement, [Formula: see text], [Formula: see text] and [Formula: see text]. On MIT-BIH Normal Sinus Rhythm Database involvement, [Formula: see text], [Formula: see text] and [Formula: see text]. Compared with other [Formula: see text] peaks detection methods, the proposed algorithm is simple, efficient and robust.

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