[HTML][HTML] Advancements in AI for cardiac arrhythmia detection: A comprehensive overview
Cardiovascular diseases (CVDs) are a global health concern, demanding advanced
healthcare solutions. Accurate identification of CVDs via electrocardiogram (ECG) analysis …
healthcare solutions. Accurate identification of CVDs via electrocardiogram (ECG) analysis …
Emerging intelligent wearable devices for cardiovascular health monitoring
Y Wang, Y Zou, Z Li - Nano Today, 2024 - Elsevier
Cardiovascular diseases have long posed a significant threat to human health. Wearable
devices are increasingly vital in cardiovascular health monitoring, disease screening, and …
devices are increasingly vital in cardiovascular health monitoring, disease screening, and …
Multiclass ECG signal analysis using global average-based 2-D convolutional neural network modeling
Cardiovascular diseases have been reported to be the leading cause of mortality across the
globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of …
globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of …
Gsmd-srst: Group sparse mode decomposition and superlet transform based technique for multi-level classification of cardiac arrhythmia
Cardiac arrhythmia is caused due to the irregularity of the heartbeat and heart rhythm, which
increases the complications leading to the risk of heart strokes. Atrial fibrillation (AF) and …
increases the complications leading to the risk of heart strokes. Atrial fibrillation (AF) and …
Multi-source deep feature fusion for medical image analysis
In image fusion, several images are combined into one image that contains information from
all input images. In medical image analysis, image fusion can help to improve the accuracy …
all input images. In medical image analysis, image fusion can help to improve the accuracy …
Ensemble classifier fostered detection of arrhythmia using ECG data
M Ramkumar, M Alagarsamy, A Balakumar… - Medical & Biological …, 2023 - Springer
Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and
function of the human heart. This is broadly employed in heart disease detection including …
function of the human heart. This is broadly employed in heart disease detection including …
[HTML][HTML] An electrocardiogram signal classification using a hybrid machine learning and deep learning approach
An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the
heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which …
heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which …
Machine Learning and Artificial Intelligence Risk
The development of artificial intelligence is reviewed. It includes a variety of computer-driven
methods. Those applying to risk management data mining applications are reviewed, to …
methods. Those applying to risk management data mining applications are reviewed, to …
Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms.
B Omarov, M Baikuvekov… - Computers …, 2023 - search.ebscohost.com
Heart disease is a leading cause of mortality worldwide. Electrocardiograms (ECG) play a
crucial role in diagnosing heart disease. However, interpreting ECGsignals necessitates …
crucial role in diagnosing heart disease. However, interpreting ECGsignals necessitates …
An adaptive Marine Predator Optimization Algorithm (MPOA) integrated Gated Recurrent Neural Network (GRNN) classifier model for arrhythmia detection
R Pashikanti, CY Patil, SA Anirudhe - Biomedical Signal Processing and …, 2024 - Elsevier
Cardiovascular disorders are typically diagnosed using an Electrocardiogram (ECG). It is a
painless method that mimics the cyclical contraction and relaxation of the heart's muscles …
painless method that mimics the cyclical contraction and relaxation of the heart's muscles …