Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual
analysis of ECG signals is challenging due to the complex task of identifying and …

Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing
essential information on electrical hearth activity. However, inherent noise and limited …

Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly
sensitive in detecting acute heart attacks. However, due to the lengthy nature of ECG …

This research introduces an innovative ensemble approach, combining Deep Residual
Networks (ResNets) and Bidirectional Gated Recurrent Units (BiGRU), augmented with an …

Heart arrhythmia is a life-threatening cardiological disorder that attacks due to imbalances in
the heart's pulse rhythms. In this paper, we proposed a hybrid improved search ability-based …

This research possesses the producing and taking the assessment of a new deep learning
structure, namely, Deep Convolutional BiLSTM Hybrid Network, specialized for arrhythmia …

Cardiac arrhythmia is a significant health concern, necessitating timely and precise
diagnosis to prevent life-threatening complications. This study investigates the potential of …

Abstract [eng] One of the most important public health tasks is to ensure early diagnosis of
heart rhythm disorders to reduce the Disability Adjusted Life Years (DALY) index (1) …

Abstract Convolutional Neural Networks (CNNs) are often criticized for their lack of
transparency, acting as' black boxes' in decision-making, a challenge compounded by class …

Heart disease prediction is the leading cause of death globally at the moment, and its
prevalence is rising. There are difficulties in identifying heart disease in its earliest stages …