scholarly journals Premature Ventricular Contraction Arrhythmia Detection in ECG Signals via Combined Classifiers

2018 ◽  
Vol 15 (1) ◽  
pp. 55-70
Author(s):  
Masoud Rahbaripour ◽  
Babak Mohammadzadeh Asl ◽  
◽  
Keyword(s):  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Arrhythmia Detection ◽  
Ecg Signals

scholarly journals Identification of Premature Ventricular Contraction (PVC) Based on ECG Using Convolutional Neural Network

2020 ◽  
Vol 9 (1) ◽  
pp. 2173-2177
Keyword(s):  
Neural Network ◽  
Performance Measures ◽  
Premature Ventricular Contraction ◽  
High Accuracy ◽  
Vital Role ◽  
Ventricular Contraction ◽  
Effective Technique ◽  
Ecg Signals ◽  
Traditional Approaches

Premature Ventricular Contraction (PVC) arrhythmia patients are subjected to dangerous heart rhythms that can be chaotic, and possibly result in abrupt death. Therefore, early detection of arrhythmia with high accuracy is extremely important to detect cardiovascular diseases. The classification of heartbeats based on ECG signals plays a vital role it the field of cardiac sciences to identify arrhythmias. The use of Artificial Neural Networks (ANN) has proven to be the most effective technique for sole agenda of classification. The use of CNN is simple and more noise immune method in comparison to various other techniques. In this paper, a survey of numerous algorithms and classification techniques along with their performance measures are presented. This paper proposes the identification of PVC on the basis of heart beats by using CNN and the results obtained are compared to other traditional approaches

scholarly journals ECG Beat Classification using a Sliding Window and Correlation of the Three-bit Linear Prediction Error Signal

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Prediction Error ◽  
Linear Prediction ◽  
Sudden Cardiac Arrest ◽  
Premature Ventricular Contraction ◽  
Sliding Window ◽  
Error Signal ◽  
Ventricular Contraction ◽  
Ecg Signals ◽  
Beat Classification

Sudden cardiac arrest (SCA) is responsible for half of all deaths due to heart disease. Most SCAs could be avoided by obtaining an early diagnosis from ECG recordings. The long-term monitoring systems record a large number of beats and require automatic detection and classification of the premature ventricular contraction (PVC) beats. Several ECG beat classification algorithms based on different methodologies have been developed and implemented. This paper presents a novel algorithm for automatic recognition of a premature ventricular contraction (PVC) beat based on a three-bit linear prediction error signal (LPES). The algorithm is composed of three main stages: signal denoising and QRS detection; nonlinear transformation of the linear prediction error signal e(n); and a sliding window. The proposed algorithm was tested using ECG signals from two recognized arrhythmia databases, MIT-BIH and AHA. The selected signals contained normal beats as well as abnormal beats. Sensitivity and specificity parameters were used to measure the accuracy of the proposed classifier. The sensitivity achieved using the proposed algorithm was 96.3% and the specificity was 99.0%. In addition to its accuracy, the main advantages of using the proposed algorithm are its simplicity and robustness.

scholarly journals Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy

2018 ◽  
Vol 7 (4.44) ◽  
pp. 161 ◽  
Author(s):  
Achmad Rizal ◽  
Riandini . ◽  
Teni Tresnawati
Keyword(s):  
Premature Ventricular Contraction ◽  
High Accuracy ◽  
Entropy Method ◽  
Ecg Signal ◽  
Median Filtering ◽  
Ventricular Contraction ◽  
Mother Wavelet ◽  
Wavelet Entropy ◽  
Ecg Signals

One of the abnormalities in the heart that can be assessed from an ECG signal is premature ventricle contraction (PVC). PVC is a form of arrhythmia in the form of irregularity in beat ECG signals. In this study, a multilevel wavelet entropy method was developed to distinguish PVC and normal ECG signals automatically. Data was taken from the MIT-BIH arrhythmia database with the process carried out is normalization, median filtering, beat-parsing, MWE calculation and classification using SVM. The results of the experiment showed that MWE level 5 with DB2 as mother wavelet and Quadratic SVM as classifier resulted in the highest accuracy of 94.9%. MWE level 5 means only five features needed for classification. The number of features is very little compared to previous research with a quite high accuracy.  

scholarly journals CARDIAC AFFERENT SIGNALING PARTIALLY UNDERLIES PREMATURE VENTRICULAR CONTRACTION-INDUCED CARDIOMYOPATHY

2021 ◽  
Vol 77 (18) ◽  
pp. 579
Author(s):  
Yuichi Hori ◽  
Taro Temma ◽  
Christian Wooten ◽  
Christopher O Sobowale ◽  
Christopher Chan ◽  
...  
Keyword(s):  
Premature Ventricular Contraction ◽  
Ventricular Contraction ◽  
Afferent Signaling

scholarly journals Robust deep learning pipeline for PVC beats localization

2021 ◽  
Vol 29 ◽  
pp. 475-486
Author(s):  
Bohdan Petryshak ◽  
Illia Kachko ◽  
Mykola Maksymenko ◽  
Oles Dobosevych
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Premature Ventricular Contraction ◽  
Ecg Signal ◽  
Ventricular Contraction ◽  
Localization Task ◽  
Robust Performance ◽  
Extensive Evaluation ◽  
Decoder Architecture ◽  
Public Datasets

BACKGROUND: Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats. OBJECTIVE: The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. METHODS: Our method consists of two neural networks. First, an encoder-decoder architecture trained on PVC-rich dataset localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model does the delineation of healthy versus PVC beats. RESULTS: We have performed an extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task. CONCLUSIONS: We have shown a method that provides robust performance beyond the beats of Normal nature and clearly outperforms classical algorithms both in the case of a single and cross-dataset evaluation. We provide a Github1 repository for the reproduction of the results.

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