Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …

Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity
produced by the contraction and relaxation of the cardiac muscles. It has been established …

Cardiovascular diseases (CVDs) are a global health concern, demanding advanced
healthcare solutions. Accurate identification of CVDs via electrocardiogram (ECG) analysis …

Objective Predicting mortality after acute myocardial infarction (AMI) is crucial for timely
prescription and treatment of AMI patients, but there are no appropriate AI systems for …

The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection
is still a challenge for public health and motivates researchers to improve methods for …

The classification of ECG signals is a critical process because it guides the diagnosis of the
proper treatment process for the patient. However, any form of disturbance with ECG signals …

Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart
arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed …

In our research, we address the problem of coordination and planning in heterogeneous
multi-robot systems for missions that consist of spatially localized tasks. Conventionally, this …

Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health
risks, including stroke and heart failure, making accurate and early detection critical for …

Feature attribution methods can visually highlight specific input regions containing influential
aspects affecting a deep learning model's prediction. Recently, the use of feature attribution …