Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances
Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the
cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first …
cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first …
Data preprocessing for heart disease classification: A systematic literature review
H Benhar, A Idri, JL Fernández-Alemán - Computer Methods and Programs …, 2020 - Elsevier
Context Early detection of heart disease is an important challenge since 17.3 million people
yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac …
yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac …
Prediction of heart disease using a combination of machine learning and deep learning
The correct prediction of heart disease can prevent life threats, and incorrect prediction can
prove to be fatal at the same time. In this paper different machine learning algorithms and …
prove to be fatal at the same time. In this paper different machine learning algorithms and …
Classification models for heart disease prediction using feature selection and PCA
AK Gárate-Escamila, AH El Hassani… - Informatics in Medicine …, 2020 - Elsevier
The prediction of cardiac disease helps practitioners make more accurate decisions
regarding patients' health. Therefore, the use of machine learning (ML) is a solution to …
regarding patients' health. Therefore, the use of machine learning (ML) is a solution to …
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time
traces collected from surface recordings over the heart. Here we hypothesized that a deep …
traces collected from surface recordings over the heart. Here we hypothesized that a deep …
[HTML][HTML] ECG-based heartbeat classification for arrhythmia detection: A survey
An electrocardiogram (ECG) measures the electric activity of the heart and has been widely
used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing …
used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing …
ECG classification using wavelet packet entropy and random forests
T Li, M Zhou - Entropy, 2016 - mdpi.com
The electrocardiogram (ECG) is one of the most important techniques for heart disease
diagnosis. Many traditional methodologies of feature extraction and classification have been …
diagnosis. Many traditional methodologies of feature extraction and classification have been …
Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography
S Narula, K Shameer, AM Salem Omar… - Journal of the American …, 2016 - jacc.org
Background: Machine-learning models may aid cardiac phenotypic recognition by using
features of cardiac tissue deformation. Objectives: This study investigated the diagnostic …
features of cardiac tissue deformation. Objectives: This study investigated the diagnostic …
Classification of ECG arrhythmia using recurrent neural networks
Abstract In this paper, Recurrent Neural Networks (RNN) have been applied for classifying
the normal and abnormal beats in an ECG. The primary aim of this paper was to enable …
the normal and abnormal beats in an ECG. The primary aim of this paper was to enable …
ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network
Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance
for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods …
for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods …