[HTML][HTML] Analysis of various techniques for ECG signal in healthcare, past, present, and future
Cardiovascular diseases are the primary reason for mortality worldwide. As per WHO survey
report in 2019, 17.9 million people died due to CVDs, accounting for 32% of all global …
report in 2019, 17.9 million people died due to CVDs, accounting for 32% of all global …
A multitier deep learning model for arrhythmia detection
An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular
diseases (CVDs). ECG signals provide a framework to probe the underlying properties and …
diseases (CVDs). ECG signals provide a framework to probe the underlying properties and …
Ventilation diagnosis of angle grinder using thermal imaging
A Glowacz - Sensors, 2021 - mdpi.com
The paper presents an analysis and classification method to evaluate the working condition
of angle grinders by means of infrared (IR) thermography and IR image processing. An …
of angle grinders by means of infrared (IR) thermography and IR image processing. An …
Unsupervised ECG analysis: A review
Electrocardiography is the gold standard technique for detecting abnormal heart conditions.
Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the …
Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the …
An automatic arrhythmia classification model based on improved marine predators algorithm and convolutions neural networks
Abstract Preparation of Convolutional Neural Networks (CNNs) for classification purposes
depends heavily on the knowledge of hyper-parameters tuning. This study aims, in particular …
depends heavily on the knowledge of hyper-parameters tuning. This study aims, in particular …
Deep learning-based stacked denoising and autoencoder for ECG heartbeat classification
S Nurmaini, A Darmawahyuni, AN Sakti Mukti… - Electronics, 2020 - mdpi.com
The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia.
However, the ECG signal is prone to contamination by different kinds of noise. Such noise …
However, the ECG signal is prone to contamination by different kinds of noise. Such noise …
[HTML][HTML] Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks
The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
Analysis of covid-19 infections on a ct image using deepsense model
In this paper, a data mining model on a hybrid deep learning framework is designed to
diagnose the medical conditions of patients infected with the coronavirus disease 2019 …
diagnose the medical conditions of patients infected with the coronavirus disease 2019 …
A hybrid heartbeats classification approach based on marine predators algorithm and convolution neural networks
The electrocardiogram (ECG) is a non-invasive tool used to diagnose various heart
conditions. Arrhythmia is one of the primary causes of cardiac arrest. Early ECG beat …
conditions. Arrhythmia is one of the primary causes of cardiac arrest. Early ECG beat …
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network
B Tutuko, S Nurmaini, AE Tondas… - BMC Medical Informatics …, 2021 - Springer
Background Generalization model capacity of deep learning (DL) approach for atrial
fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL …
fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL …