Memristor-based echo state network for ECG classification: optimizing accuracy and efficiency with different architectures

Advanced ECG Signal Analysis Using AM-RNN and Optimized Feature Selection for Improved Cardiac Diagnosis

This work presents novel approach for ECG signal classification through an integrated Deep Learning (DL) framework that incorporates adaptable filters that adjust based on input data resulting in superior performance in classifying numerous cardiac conditions.

Multi-class electrocardiogram signal classification using deep learning and two-dimensional time-frequency codification

The automated classification of cardiac arrhythmias in electrocardiogram (ECG) signals is critical for cardiovascular diagnosis. Several studies focus on the classification of ECG rhythms. However this task remains a challenge, as is often affected by significant class imbalances, long-term temporal dependencies and fi...

Detection of Cardiovascular Diseases Using Machine Learning and Deep Learning Techniques

Present a Wavelet-CNN Hybrid (WCNN-H) framework for automatic multi-class classification of cardiovascular diseases from ECG images, which has a high potential for intelligent computer aided cardiovascular screening applications.

Running a Deep ECG Model on Low-Cost Hardware.

Hybrid Multi-Domain ECG Feature Learning with mRMR and CNN–Transformer for Cardiac Disease Classification

Cardiovascular disease diagnosis requires accurate and timely analysis of electrocardiogram (ECG) signals to support reliable clinical decision-making. However, ECG signals are inherently non-stationary, exhibit substantial inter-patient variability, and may share similar morphological patterns across different cardiac...

A hybrid deep feature extraction and ensemble learning framework for robust multi-class ECG image classification

ECG signals, the electrical activity of the heart, play a vital role in early identification of cardiovascular diseases (CVDs), which are currently the leading cause of death across the world. However, manual detection of ECG images can be time-consuming, prone to human error, and difficult to scale, particularly in...