Detection and classification of arrhythmia using an explainable deep learning model
Background Early detection and intervention is the cornerstone for appropriate treatment of
arrhythmia and prevention of complications and mortality. Although diverse deep learning …
arrhythmia and prevention of complications and mortality. Although diverse deep learning …
Interpretation and classification of arrhythmia using deep convolutional network
P Singh, A Sharma - IEEE Transactions on Instrumentation and …, 2022 - ieeexplore.ieee.org
Electrocardiogram (ECG) signal analysis can be time-consuming, tedious, and error-prone.
Therefore, automated analysis is need of time that will assist clinicians in detecting cardiac …
Therefore, automated analysis is need of time that will assist clinicians in detecting cardiac …
DeepArr: An investigative tool for arrhythmia detection using a contextual deep neural network from electrocardiograms (ECG) signals
In the context of Cardiovascular Diseases, arrhythmia is one of the causes of sudden death,
which is related to abnormal electrical activities of the heart that can be reflected by the …
which is related to abnormal electrical activities of the heart that can be reflected by the …
Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram
Introduction Early detection and intervention of atrial fibrillation (AF) is a cornerstone for
effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have …
effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have …
Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance Evaluation
Heart diseases is the world's principal cause of death, and arrhythmia poses a serious risk to
the health of the patient. Electrocardiogram (ECG) signals can be used to detect arrhythmia …
the health of the patient. Electrocardiogram (ECG) signals can be used to detect arrhythmia …
Cardiac arrhythmia detection using deep learning: A review
Due to its simplicity and low cost, analyzing an electrocardiogram (ECG) is the most
common technique for detecting cardiac arrhythmia. The massive amount of ECG data …
common technique for detecting cardiac arrhythmia. The massive amount of ECG data …
Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023
Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography
(ECG) still represents the benchmark approach for identifying cardiac irregularities …
(ECG) still represents the benchmark approach for identifying cardiac irregularities …
Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records
Background and objective Cardiac arrhythmia, which is an abnormal heart rhythm, is a
common clinical problem in cardiology. Detection of arrhythmia on an extended duration …
common clinical problem in cardiology. Detection of arrhythmia on an extended duration …
Heartbeat classification and arrhythmia detection using a multi-model deep-learning technique
Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost
importance to be able to efficiently diagnose these arrhythmias promptly. There exist many …
importance to be able to efficiently diagnose these arrhythmias promptly. There exist many …
A lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection
N Alamatsaz, L Tabatabaei, M Yazdchi, H Payan… - … Signal Processing and …, 2024 - Elsevier
Objective: Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for
monitoring heart electrical signals and evaluating its functionality. The human heart can …
monitoring heart electrical signals and evaluating its functionality. The human heart can …