A survey on ECG analysis
The electrocardiogram (ECG) signal basically corresponds to the electrical activity of the
heart. In the literature, the ECG signal has been analyzed and utilized for various purposes …
heart. In the literature, the ECG signal has been analyzed and utilized for various purposes …
ECG signals classification: a review
EH Houssein, M Kilany… - International Journal of …, 2017 - inderscienceonline.com
Electrocardiogram (ECG), non-stationary signals, is extensively used to evaluate the rate
and tuning of heartbeats. The main purpose of this paper is to provide an overview of …
and tuning of heartbeats. The main purpose of this paper is to provide an overview of …
A deep learning approach for real-time detection of atrial fibrillation
RS Andersen, A Peimankar… - Expert Systems with …, 2019 - Elsevier
Goal: To develop a robust and real-time approach for automatic detection of atrial fibrillation
(AF) in long-term electrocardiogram (ECG) recordings using deep learning (DL). Method: An …
(AF) in long-term electrocardiogram (ECG) recordings using deep learning (DL). Method: An …
[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 …
Plant classification using convolutional neural networks
Application of the benefits of modern computing technology to improve the efficiency of
agricultural fields is inevitable with growing concerns about increasing world population and …
agricultural fields is inevitable with growing concerns about increasing world population and …
DENS-ECG: A deep learning approach for ECG signal delineation
A Peimankar, S Puthusserypady - Expert systems with applications, 2021 - Elsevier
Objectives With the technological advancements in the field of tele-health monitoring, it is
now possible to gather huge amount of electro-physiological signals such as the …
now possible to gather huge amount of electro-physiological signals such as the …
Classification of ECG signals using machine learning techniques: A survey
SH Jambukia, VK Dabhi… - … Conference on Advances …, 2015 - ieeexplore.ieee.org
Classification of electrocardiogram (ECG) signals plays an important role in diagnoses of
heart diseases. An accurate ECG classification is a challenging problem. This paper …
heart diseases. An accurate ECG classification is a challenging problem. This paper …
Sparse representation of ECG signals for automated recognition of cardiac arrhythmias
S Raj, KC Ray - Expert systems with applications, 2018 - Elsevier
As per the report of the World Health Organization (WHO), the mortalities due to
cardiovascular diseases (CVDs) have increased to 50 million worldwide. Therefore, it is …
cardiovascular diseases (CVDs) have increased to 50 million worldwide. Therefore, it is …
ECG arrhythmia classification using a probabilistic neural network with a feature reduction method
This paper presents an effective electrocardiogram (ECG) arrhythmia classification scheme
consisting of a feature reduction method combining principal component analysis (PCA) with …
consisting of a feature reduction method combining principal component analysis (PCA) with …
ECG beat classification using particle swarm optimization and radial basis function neural network
M Korürek, B Doğan - Expert systems with Applications, 2010 - Elsevier
This paper presents a method for electrocardiogram (ECG) beat classification based on
particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six …
particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six …