In this work, a hybrid model is proposed for ECG signal analysis to classify SVEB and VEB arrhythmia classes. The proposed model is evaluated on the MIT-BIH ...

This paper proposes a hybrid deep learning-based approach to automate the detection and classification process.

Apr 2, 2022 · The obtained results show that the proposed approach provides ≈98.7%, 99%, and 99% accuracy for Cardiac Arrhythmias (ARR), Congestive Heart ...

Oct 18, 2024 · This work proposes a new hybrid deep learning model that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with multi- ...

In this work, a hybrid model is proposed for ECG signal analysis to classify SVEB and VEB arrhythmia classes. The proposed model is evaluated on the MIT-BIH ...

Oct 19, 2023 · The main objective of this study was to create an automated deep learning model capable of accurately classifying ECG signals into three categories.

This work proposes a hybrid deep learning model, Res-BiANet, designed for the detection and classification of multiple types of arrhythmias.

Nov 6, 2024 · This study uses AI models like AlexNet and a dual branch model for categorizing ECG signals from the PTB Diagnostic ECG Database.

Aug 29, 2022 · This paper introduces a light deep learning approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythm.

Sep 14, 2023 · This survey categorizes and compares the DL architectures used in ECG arrhythmia detection from 2017–2023 that have exhibited superior performance.