Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures
AS Eltrass, MB Tayel, AI Ammar - Neural Computing and Applications, 2022 - Springer
Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several
types of heart disorders. In this study, a novel hybrid approach of deep neural network …
types of heart disorders. In this study, a novel hybrid approach of deep neural network …
Deep convolutional neural network based ECG classification system using information fusion and one‐hot encoding techniques
J Li, Y Si, T Xu, S Jiang - Mathematical problems in engineering, 2018 - Wiley Online Library
Although convolutional neural networks (CNNs) can be used to classify electrocardiogram
(ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed …
(ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed …
Cardiac disorder classification by electrocardiogram sensing using deep neural network
Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be
prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to …
prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to …
Deep learning in ECG diagnosis: A review
X Liu, H Wang, Z Li, L Qin - Knowledge-Based Systems, 2021 - Elsevier
Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …
Evaluation of handcrafted features and learned representations for the classification of arrhythmia and congestive heart failure in ECG
Electrocardiogram (ECG) is considered as an essential diagnostic tool to investigate life-
threatening cardiac abnormalities, such as arrhythmia and congestive heart failure. It is …
threatening cardiac abnormalities, such as arrhythmia and congestive heart failure. It is …
Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
P Pławiak, UR Acharya - Neural Computing and Applications, 2020 - Springer
The heart disease is one of the most serious health problems in today's world. Over 50
million persons have cardiovascular diseases around the world. Our proposed work based …
million persons have cardiovascular diseases around the world. Our proposed work based …
ECG beat classification based on discriminative multilevel feature analysis and deep learning approach
N Sinha, RK Tripathy, A Das - Biomedical Signal Processing and Control, 2022 - Elsevier
Extraction of significant features from Electrocardiogram (ECG) signal is the primary concern
for accurate diagnosis of cardiac arrhythmia. This work presents a novel approach of …
for accurate diagnosis of cardiac arrhythmia. This work presents a novel approach of …
Arrhythmia classification techniques using deep neural network
Electrocardiogram (ECG) is the most common and low‐cost diagnostic tool used in
healthcare institutes for screening heart electrical signals. The abnormal heart signals are …
healthcare institutes for screening heart electrical signals. The abnormal heart signals are …
ECG classification using 1-D convolutional deep residual neural network
An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular
diseases (CVDs). The traditional ECG classification methods have complex signal …
diseases (CVDs). The traditional ECG classification methods have complex signal …
A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals
This work presents an efficient hybridized approach for the classification of
electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat …
electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat …