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 …
Arrhythmia detection and classification using ECG and PPG techniques: A review
Electrocardiogram (ECG) and photoplethysmograph (PPG) are non-invasive techniques that
provide electrical and hemodynamic information of the heart, respectively. This information …
provide electrical and hemodynamic information of the heart, respectively. This information …
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 …
Deep learning approach for active classification of electrocardiogram signals
In this paper, we propose a novel approach based on deep learning for active classification
of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation …
of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation …
A hybrid deep CNN model for abnormal arrhythmia detection based on cardiac ECG signal
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients
suffering from various cardiovascular diseases (CVDs). This research aims to develop a …
suffering from various cardiovascular diseases (CVDs). This research aims to develop a …
[HTML][HTML] Inter-patient arrhythmia classification with improved deep residual convolutional neural network
Y Li, R Qian, K Li - Computer Methods and Programs in Biomedicine, 2022 - Elsevier
Abstract Background and Objective: Early detection of arrhythmias has become critical due
to the increased mortality from cardiovascular disease, and ECG is an effective tool for …
to the increased mortality from cardiovascular disease, and ECG is an effective tool for …
A study on arrhythmia via ECG signal classification using the convolutional neural network
M Wu, Y Lu, W Yang, SY Wong - Frontiers in computational …, 2021 - frontiersin.org
Cardiovascular diseases (CVDs) are the leading cause of death today. The current
identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a …
identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a …
DENS-ECG: A deep learning approach for ECG signal delineation
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 …
Deep multi-scale fusion neural network for multi-class arrhythmia detection
Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in
early prevention and diagnosis of cardiovascular diseases. Extracting powerful features from …
early prevention and diagnosis of cardiovascular diseases. Extracting powerful features from …
Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine
W Yang, Y Si, D Wang, B Guo - Computers in biology and medicine, 2018 - Elsevier
Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and
neural network models have been widely used in this field. However, these models are often …
neural network models have been widely used in this field. However, these models are often …