Deep learning-assisted arrhythmia classification using 2-D ECG spectrograms
Abstract This article studies modern classification techniques in ECG signals through the transfer learning approach with CNN (Convolutional Neural Network). The proposed pre-trained network combines an Imagenet with huge labeled image datasets and a separate network composed of fully connected laye...
Saved in:
Main Authors: | Pinjala N Malleswari, Venkata krishna Odugu, T. J. V. Subrahmanyeswara Rao, T. V. N. L. Aswini |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2024-12-01
|
Series: | EURASIP Journal on Advances in Signal Processing |
Subjects: | |
Online Access: | https://doi.org/10.1186/s13634-024-01197-1 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Classification of ECG signals using deep neural networks
by: Nadour Mohamed, et al.
Published: (2023-06-01) -
ECGConVT: A Hybrid CNN and Vision Transformer Model for Enhanced 12-Lead ECG Images Classification
by: Mudassar Khalid, et al.
Published: (2024-01-01) -
An improved electrocardiogram arrhythmia classification performance with feature optimization
by: Annisa Darmawahyuni, et al.
Published: (2024-12-01) -
ECG-based transfer learning for cardiovascular disease: A scoping review
by: Sharifah Noor Masidayu Sayed Ismail, et al.
Published: (2025-12-01) -
Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning
by: Oleksii Kovalchuk, et al.
Published: (2025-01-01)