scholarly journals Automatic Classification of Cardiac Arrhythmias based on ECG Signals Using Transferred Deep Learning Convolution Neural Network

2021 ◽  
Vol 2089 (1) ◽  
pp. 012058
Author(s):  
P. Giriprasad Gaddam ◽  
A Sanjeeva reddy ◽  
R.V. Sreehari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Automatic Classification ◽  
Ecg Signals ◽  
Major Interest

Abstract In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Four different categories of ECG waveform were selected from four PhysioNet MIT-BIH databases, namely arrhythmia database, Normal Sinus Rhythm database, Malignant Ventricular Ectopy database and BIDMC Congestive heart failure database to examine the proposed technique. The major interest of the present study is to develop a transferred deep learning algorithm for automatic categorization of the mentioned four different heart diseases. Final results proved that the 2-D scalogram images trained with a deep convolutional neural network CNN with transfer learning technique (AlexNet) pepped up with a prominent accuracy of 95.67%. Hence, it is worthwhile to say the above stated algorithm demonstrates as an effective automated heart disease detection tool

Early Detection Of Life-Threatening Cardiac Arrhythmias Using Deep Learning Techniques

2019 ◽  
Vol 14 ◽  
Author(s):  
Sumathi S ◽  
Agalya V
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Clinical Training ◽  
Normal Sinus Rhythm ◽  
False Negative ◽  
Learning Model ◽  
Detection Scheme ◽  
Ecg Signals ◽  
Deep Learning Model

: A progressive and flourishing technological advancement occurs across the communities working on a domain that needs clinical training and Technology Transfer. There is an essentiality for the evolution of advanced concepts in the Classification of healthcare, particularly in relation to arrhythmia detection towards clinical operations. Being the forerunner among the emerging areas in science and technology, this field demands an extensive practical and verification research. These innovative technological progress has significantly contributed to high-quality, on-time, acceptable and affordable healthcare. This paper approaches a novel method of Detecting and classifying the cardiac arrhythmias using deep learning model for classification of electrocardiogram (ECG) signals. This method is based on using Cubic Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these parameters ar used as input to these classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU). By using the deep learning algorithm to recognition and classification capabilities across a broad area of biomedical engineering. The performance of the deep learning model was evaluated in terms of training performance and classification accuracies. The classification accuracy of 99.24% is achieved. . Good accuracy of ECG patterns is achievable only over a large number of files.These difficulties have necessitated us to develop a new detection scheme, which gives a high level of accuracy, low false-positive and low false-negative statistics.

scholarly journals Convolutional Neural Network for Classification of Eight types of Arrhythmia using 2D Time-Frequency Feature Map from Standard 12-Lead Electrocardiogram

2021 ◽  
Author(s):  
Da Un Jeong ◽  
Ki Moo Lim
Keyword(s):  
Cardiac Arrhythmia ◽  
Cardiac Arrhythmias ◽  
Clustering Algorithm ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Feature Maps ◽  
Time Frequency ◽  
Ecg Signals ◽  
Frequency Feature

Abstract Electrocardiograms (ECGs) are widely used for diagnosing cardiac arrhythmia based on the deformation of signal shapes due to changes in various heart diseases. However, these abnormal signs may not be observed in some of the 12 ECG channels, depending on the location or shape of the heart and the type of cardiac arrhythmia. Therefore, to accurately diagnose cardiac arrhythmias, it is necessary to closely and comprehensively observe ECG signals acquired from 12 channel electrodes. In this study, we proposed a clustering algorithm that can classify persistent cardiac arrhythmia as well as episodic cardiac arrhythmias using the standard 12-lead ECG signals and the 2D CNN model using the time-frequency feature maps to classify the eight types of arrhythmias including normal sinus rhythm. The standard 12-lead ECG dataset was provided by Computing in Cardiology 2020 Physionet Challenge and consisted of 6,877 patients. The proposed algorithm showed excellent performance in the classification of persistent cardiac arrhythmias; however, its accuracy was somewhat low in the classification of episodic arrhythmias. If our proposed model is trained and verified using more clinical data, we believe it can be used as an auxiliary device for diagnosing cardiac arrhythmias.

scholarly journals Deep Extreme Learning Machine and Its Application in EEG Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shifei Ding ◽  
Nan Zhang ◽  
Xinzheng Xu ◽  
Lili Guo ◽  
Jian Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Eeg Classification ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Learning Machine

Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM) put forward deep extreme learning machine (DELM) and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI) competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed.

A comparative analysis of euphemistic sentences in news using feature weight scheme and intelligent techniques

2021 ◽  
pp. 1-12
Author(s):  
K. Seethappan ◽  
K. Premalatha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Automatic Classification ◽  
Weighting Scheme ◽  
Feature Weighting ◽  
Classification Algorithms ◽  
Weighting Schemes

Although there have been various researches in the detection of different figurative language, there is no single work in the automatic classification of euphemisms. Our primary work is to present a system for the automatic classification of euphemistic phrases in a document. In this research, a large dataset consisting of 100,000 sentences is collected from different resources for identifying euphemism or non-euphemism utterances. In this work, several approaches are focused to improve the euphemism classification: 1. A Combination of lexical n-gram features 2.Three Feature-weighting schemes 3.Deep learning classification algorithms. In this paper, four machine learning (J48, Random Forest, Multinomial Naïve Bayes, and SVM) and three deep learning algorithms (Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory) are investigated with various combinations of features and feature weighting schemes to classify the sentences. According to our experiments, Convolutional Neural Network (CNN) achieves precision 95.43%, recall 95.06%, F-Score 95.25%, accuracy 95.26%, and Kappa 0.905 by using a combination of unigram and bigram features with TF-IDF feature weighting scheme in the classification of euphemism. These results of experiments show CNN with a strong combination of unigram and bigram features set with TF-IDF feature weighting scheme outperforms another six classification algorithms in detecting the euphemisms in our dataset.

scholarly journals Artificial Neural Network-Based Automated ECG Signal Classifier

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Sahar H. El-Khafif ◽  
Mohamed A. El-Brawany
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Learning Algorithm ◽  
Normal Sinus Rhythm ◽  
Learning Rule ◽  
Higher Order ◽  
Ischemic Heart ◽  
Ecg Signal ◽  
Ecg Signals

The ECG signal is well known for its nonlinear dynamic behavior and a key characteristic that is utilized in this research; the nonlinear component of its dynamics changes more significantly between normal and abnormal conditions than does the linear one. As the higher-order statistics (HOS) preserve phase information, this study makes use of one-dimensional slices from the higher-order spectral domain of normal and ischemic subjects. A feedforward multilayer neural network (NN) with error back-propagation (BP) learning algorithm was used as an automated ECG classifier to investigate the possibility of recognizing ischemic heart disease from normal ECG signals. Different NN structures are tested using two data sets extracted from polyspectrum slices and polycoherence indices of the ECG signals. ECG signals from the MIT/BIH CD-ROM, the Normal Sinus Rhythm Database (NSR-DB), and European ST-T database have been utilized in this paper. The best classification rates obtained are 93% and 91.9% using EDBD learning rule with two hidden layers for the first structure and one hidden layer for the second structure, respectively. The results successfully showed that the presented NN-based classifier can be used for diagnosis of ischemic heart disease.

scholarly journals Classification of Atrial Fibrillation and Congestive Heart Failure using Convolutional Neural Network with Electrocardiogram

2021 ◽  
Author(s):  
Yunendah Nur Fu’adah ◽  
Ki Moo Lim
Keyword(s):  
Neural Network ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Congestive Heart Failure ◽  
Convolutional Neural Network ◽  
Classification System ◽  
Normal Sinus Rhythm ◽  
Delayed Diagnosis ◽  
Ecg Signals

Abstract Delayed diagnosis of atrial fibrillation (AF) and congestive heart failure (CHF) can lead to death. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. Several studies have reported promising results using the automatic classification of ECG signals. The performance accuracy needs to be improved considering that an accurate classification system of AF and CHF has the potential to save a patient’s life. An optimal ECG signal classification system for AF and CHF has been proposed in this study using a one-dimensional convolutional neural network (1-D CNN) to improve the performance. A total of 150 datasets of ECG signals were modeled using the1-D CNN. The proposed 1-D CNN algorithm, provided precision values, recall, f1-score, accuracy of 100%, and successfully classified raw data of ECG signals into three conditions, which are normal sinus rhythm (NSR), AF, and CHF. The results showed that the proposed method outperformed the previous methods. This approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.

Deep Learning Algorithm for Image Classification of Waveforms Obtained From Electrically Stimulated Hypoxic Skeletal Muscle Bundles

2020 ◽  
Author(s):  
Weston Upchurch ◽  
Alex Deakyne ◽  
David A. Ramirez ◽  
Paul A. Iaizzo
Keyword(s):  
Neural Network ◽  
Skeletal Muscle ◽  
Deep Learning ◽  
Emergency Treatment ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Hypoxic Conditions ◽  
Deep Learning Algorithm ◽  
Compartmental Pressure

Abstract Acute compartment syndrome is a serious condition that requires urgent surgical treatment. While the current emergency treatment is straightforward — relieve intra-compartmental pressure via fasciotomy — the diagnosis is often a difficult one. A deep neural network is presented here that has been trained to detect whether isolated muscle bundles were exposed to hypoxic conditions and became ischemic.

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