Hyperellipsoidal Classifier for Beat Classification in ECG Signals

2011 ◽  
pp. 219-236
Author(s):  
G. Bortolan ◽  
I. Christov ◽  
W. Pedrycz
Keyword(s):  
Ecg Signals ◽  
Beat Classification

ELECTROCARDIOGRAM BEAT CLASSIFICATION USING S-TRANSFORM BASED FEATURE SET

2014 ◽  
Vol 14 (05) ◽  
pp. 1450066 ◽  
Author(s):  
MANAB KUMAR DAS ◽  
SAMIT ARI
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Massachusetts Institute Of Technology ◽  
Ecg Signals ◽  
Beat Classification ◽  
Temporal Features ◽  
S Transform ◽  
Institute Of Technology ◽  
Stockwell Transform

In this paper, the conventional Stockwell transform is effectively used to classify the ECG arrhythmias. The performance of ECG classification mainly depends on feature extraction based on an efficient formation of morphological and temporal features and the design of the classifier. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not selected properly. Here, the S-transform (ST) is used to extract the morphological features which is appended with temporal features. This feature set is independently classified using artificial neural network (NN) and support vector machine (SVM). In this work, five classes of ECG beats (normal, ventricular, supra ventricular, fusion and unknown beats) from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database are classified according to AAMI EC57 1998 standard (Association for the Advancement of Medical Instrumentation). Performance is evaluated on several normal and abnormal ECG signals of MIT-BIH arrhythmias database using two classifier techniques: ST with NN classifier (ST-NN) and other proposed ST with SVM classifier (ST-SVM). The proposed method achieves accuracy of 98.47%. The performance of the proposed technique is compared with ST-NN and earlier reported technique.

scholarly journals Feature Selection for ECG Beat Classification using Genetic Algorithms

2018 ◽  
pp. 149-156
Author(s):  
Cagla Sarvan ◽  
Nalan Ozkurt ◽  
Korhan Karabulut
Keyword(s):  
Genetic Algorithm ◽  
Discrete Wavelet ◽  
Ecg Signals ◽  
Beat Classification ◽  
Genetic Algorithm Method ◽  
Selection For ◽  
Ecg Arrhythmia Classification ◽  
Ecg Arrhythmia ◽  
Different Levels

In this study, genetic algorithm method was used to select the most suitable set of features for classification of arrhythmia types of heart beats. Normal, right branch block, left branch block and pace rhythm samples of electrocardiography (ECG) signals which obtained from the MIT-BIH cardiac arrhythmia database were used in the classification. Mean, standard deviation, energy and entropy of discrete wavelet transform (DWT) coefficients were proposed as the features for the classification. By using the proposed DWT method, 16 features which have high classification accuracy were obtained among the 208 feature sets constructed from 13 different wavelet types by applying the genetic algorithm method. It was observed that the features that increase accuracy can be detected by the genetic algorithm and the feature set obtained from the coefficients of the different types of wavelets selected at different levels show higher performance than the coefficients obtained from the standard individual wavelet in the ECG arrhythmia classification.

scholarly journals An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Junli Gao ◽  
Hongpo Zhang ◽  
Peng Lu ◽  
Zongmin Wang
Keyword(s):  
Short Term Memory ◽  
Recurrent Network ◽  
Short Term ◽  
Ecg Signals ◽  
Beat Classification ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Reliable Solution ◽  
Electrocardiogram Ecg

To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.

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.

Design of Portable Electrocardiograph Monitoring System

2020 ◽  
Author(s):  
Jia Hua-Ping ◽  
Zhao Jun-Long ◽  
Liu Jun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Storage ◽  
P Wave ◽  
Ecg Monitoring ◽  
The Public ◽  
Ecg Signals ◽  
Early Warning Mechanism ◽  
Timely Treatment ◽  
Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

Noise Reduction in ECG Signals Using Notch Filter

2017 ◽  
Vol 5 (8) ◽  
pp. 147-150
Author(s):  
Chhavi Saxena ◽  
Hemant Kumar Gupta ◽  
P.D. Murarka
Keyword(s):  
Noise Reduction ◽  
Notch Filter ◽  
Ecg Signals
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