ANN based Classification of ECG Signals for Myocardial Infarction using Cross Correlation Concepts

2018 ◽  
Author(s):  
Akhila N. ◽  
Balamati Choudhury ◽  
Raveendranath U. Nair
Keyword(s):  
Myocardial Infarction ◽  
Cross Correlation ◽  
Ecg Signals

Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study

2017 ◽  
Vol 377 ◽  
pp. 17-29 ◽  
Author(s):  
U Rajendra Acharya ◽  
Hamido Fujita ◽  
Muhammad Adam ◽  
Oh Shu Lih ◽  
Vidya K Sudarshan ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Comparative Study ◽  
Ecg Signals ◽  
Artery Disease

Classification of myocardial infarction based on ECG signals and multi-network stacking model

2021 ◽  
Author(s):  
Tianqi Zhao ◽  
Muqing Deng ◽  
Peng Lin ◽  
Jianzhong Wang ◽  
Jiuwen Cao
Keyword(s):  
Myocardial Infarction ◽  
Ecg Signals

Feature Extraction and Classification

2014 ◽  
pp. 159-169 ◽  
Keyword(s):  
Myocardial Infarction ◽  
Noise Removal ◽  
Anterior Myocardial Infarction ◽  
Ecg Signals ◽  
Second Stage ◽  
The Third ◽  
Heart Disorders ◽  
Ecg Noise Removal

In the previous chapter, the first stage for detecting the ECG noise removal was investigated. In this chapter, the second and the third stages are explained. The Second stage is to extract the effective features of the ECG signals. The final stage is to use MLP and PSO algorithms for classification of ECG signals to detect the 4 common heart disorders including the normal signals. Common disorders are Normal, Supraventricular, Brunch bundle block, Anterior myocardial infarction (Anterior MI), and Interior myocardial infarction (Interior MI).

Classification and Feature Extraction

2018 ◽  
pp. 150-168
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Feature Extraction ◽  
Classification Performance ◽  
Feedforward Neural Network ◽  
Anterior Myocardial Infarction ◽  
Ecg Signals ◽  
Gas Molecule ◽  
Gas Molecules

In this chapter, the proposed optimization algorithm, kinetic gas molecule optimization (KGMO), that is based on swarm behaviour of gas molecules is applied to train a feedforward neural network for classification of ECG signals. Five types of ECG signals are used in this work including normal, supraventricular, brunch bundle block, anterior myocardial infarction (Anterior MI), and interior myocardial infarction (Interior MI). The classification performance of the proposed KGMO neural network (KGMONN) was evaluated on the Physiobank database and compared against conventional algorithms.

Classification of myocardial infarction with multi-lead ECG signals and deep CNN

2019 ◽  
Vol 122 ◽  
pp. 23-30 ◽  
Author(s):  
Ulas Baran Baloglu ◽  
Muhammed Talo ◽  
Ozal Yildirim ◽  
Ru San Tan ◽  
U Rajendra Acharya
Keyword(s):  
Myocardial Infarction ◽  
Ecg Signals ◽  
Deep Cnn

scholarly journals Classification of Myocardial Infarction using Convolution Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 12763-12768 ◽  
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Heart Muscle ◽  
Diagnostic Tools ◽  
Ecg Signals ◽  
Blood Tests ◽  
The People ◽  
Myocardial Infraction ◽  
Deep Learning Model

Myocardial infarction is one of the most dangerous cardiovascular diseases for most of the people in the world. It is generally confessed as a heart attack. The damage of the heart muscle causes the Myocardial Infraction (MI). When there is a block in heart veins, then the flow of oxygen to the heart muscle also gets blocked, which leads to damage of the heart muscle. The damage is irreversible, so it may lead to death. Quick and exact recognition of MI is required to reduce the death rate. There are several diagnostic tools such as blood tests, ECG is available for the analysis of cardiovascular disease. Among all tools, ECG provides effective results in determining MI, but the manual interpretation of the ECG signal may take time for the doctor to identify the symptoms of MI. The manual interpretation may vary from person to person. Hence a computer-aided diagnostic tool is required to analyze ECG signals effectively for identifying MI. This paper aims to provide an algorithm for the detection of myocardial infarction that operates directly on ECG data. Nowadays Convolutional neural network is cable of analyzing an image effectively so, a deep learning model with the CNN algorithm is used in this paper to classify the images and to identify whether the image has MI or not. The proposed CNN model yields 87% accuracy for the Physikalisch-Technische Bundesanstalt database.

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