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.

scholarly journals Study of cardiac arrhythmia classification based on Convolutional Neural Network

2020 ◽  
Vol 17 (2) ◽  
pp. 445-458
Author(s):  
Yonghui Dai ◽  
Bo Xu ◽  
Siyu Yan ◽  
Jing Xu
Keyword(s):  
Neural Network ◽  
Cardiovascular Diseases ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Time Intervals ◽  
Ecg Signals ◽  
Layer Structures ◽  
Time Periods ◽  
The One

Cardiovascular disease is one of the diseases threatening the human health, and its diagnosis has always been a research hotspot in the medical field. In particular, the diagnosis technology based on ECG (electrocardiogram) signal as an effective method for studying cardiovascular diseases has attracted many scholars? attention. In this paper, Convolutional Neural Network (CNN) is used to study the feature classification of three kinds of ECG signals, which including sinus rhythm (SR), Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF). Specifically, different convolution layer structures and different time intervals are used for ECG signal classification, such as the division of 2-layer and 4-layer convolution layers, the setting of four time periods (1s, 2s, 3s, 10s), etc. by performing the above classification conditions, the best classification results are obtained. The contribution of this paper is mainly in two aspects. On the one hand, the convolution neural network is used to classify the arrhythmia data, and different classification effects are obtained by setting different convolution layers. On the other hand, according to the data characteristics of three kinds of ECG signals, different time periods are designed to optimize the classification performance. The research results provide a reference for the classification of ECG signals and contribute to the research of cardiovascular diseases.

Methodology

2018 ◽  
pp. 81-113
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Swarm Intelligence ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Artificial Neural ◽  
Heart Disorders ◽  
Intelligent Methods

This chapter uses intelligent methods based on swarm intelligence and artificial neural network to detect heart disorders based on electrocardiogram signals. This chapter has introduced the methodology undertaken in the denoising, feature extraction, and classification of ECG signals to four heart disorders including the normal heartbeat. It also presents denoising using intelligent methods.

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).

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.

scholarly journals Classification of malignant and Benign Lung Using Probabilistic Neural Network

2019 ◽  
pp. 307-311
Author(s):  
Asmitha Shree R ◽  
Sajitha M ◽  
Subha S
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Classification Performance ◽  
Cancer Types ◽  
Extraction Stage

Lung Cancer is considered as one of the deadliest diseases among other lung disorders and cancer types and is the leading cause of cancer deaths worldwide. Lung cancer is a curable disease if detected in its early stages that makes up 13% of all cancer diagnoses and 27% of all cancer deaths. The objective of this paper is mainly focused on categorizing the patients Computed Tomography (CT) lung images as normal or abnormal. The images are subjected to segmentation to focus on detecting the cancerous region to classify. Effective feature selection and feature extraction is made by applying Watershed Transform and Principal Component Analysis. The emphasis is on the feature extraction stage to yield a better classification performance. The classification of CT images as benign or malignant is done using Machine Learning based Neural Network.

scholarly journals Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network

2019 ◽  
Vol 9 (9) ◽  
pp. 1879 ◽  
Author(s):  
Kai Feng ◽  
Xitian Pi ◽  
Hongying Liu ◽  
Kai Sun
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Rapid Development ◽  
Classification Performance ◽  
Human Beings ◽  
Ecg Signals ◽  
Good Classification Performance ◽  
Memory Network

Myocardial infarction is one of the most threatening cardiovascular diseases for human beings. With the rapid development of wearable devices and portable electrocardiogram (ECG) medical devices, it is possible and conceivable to detect and monitor myocardial infarction ECG signals in time. This paper proposed a multi-channel automatic classification algorithm combining a 16-layer convolutional neural network (CNN) and long-short term memory network (LSTM) for I-lead myocardial infarction ECG. The algorithm preprocessed the raw data to first extract the heartbeat segments; then it was trained in the multi-channel CNN and LSTM to automatically learn the acquired features and complete the myocardial infarction ECG classification. We utilized the Physikalisch-Technische Bundesanstalt (PTB) database for algorithm verification, and obtained an accuracy rate of 95.4%, a sensitivity of 98.2%, a specificity of 86.5%, and an F1 score of 96.8%, indicating that the model can achieve good classification performance without complex handcrafted features.

scholarly journals ECG Beats Classification Using Mixture of Features

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Manab Kumar Das ◽  
Samit Ari
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Ectopic Beat ◽  
Extraction Techniques ◽  
Efficient System ◽  
Ecg Signals ◽  
Temporal Features ◽  
S Transform ◽  
Electrocardiogram Ecg

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

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