Comparative study of time-frequency transformation methods for ECG signal classification
In this study, we highlighted the growing need for automated electrocardiogram (ECG) signal classification using deep learning to overcome the limitations of traditional ECG interpretation algorithms that can lead to misdiagnosis and inefficiency. Convolutional neural networks (CNN) application to E...
Main Authors: | Min-Seo Song, Seung-Bo Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2024-01-01
|
Series: | Frontiers in Signal Processing |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/frsip.2024.1322334/full |
Similar Items
-
Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model
by: Shadhon Chandra Mohonta, et al.
Published: (2022-08-01) -
1D CNN model for ECG diagnosis based on several classifiers
by: Mahmoud Bassiouni, et al.
Published: (2022-12-01) -
Wavelet transform-based multipitch estimation in polyphonic music
by: Neeraj Kumar, et al.
Published: (2020-01-01) -
Predicting Ventricular Fibrillation Through Deep Learning
by: Li-Ming Tseng, et al.
Published: (2020-01-01) -
On-Chip Acceleration of RF Signal Modulation Classification With Short-Time Fourier Transform and Convolutional Neural Network
by: Kuchul Jung, et al.
Published: (2023-01-01)