Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM
Anomaly detection is a typical binary classification problem under the condition of unbalanced samples, which has been widely used in various fields of data mining. For example, it can help detect heart murmurs when the heart is structurally abnormal, to tell if a newborn has congenital heart diseas...
Saved in:
Main Authors: | Peipei Zeng, Shuimiao Kang, Fan Fan, Jiyuan Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2025-01-01
|
Series: | Frontiers in Neuroinformatics |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2025.1530047/full |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
by: Dewei Kong, et al.
Published: (2025-01-01) -
A State-Supervised Model and Novel Anomaly Index for Gas Turbines Blade Fault Detection Under Multi-Operating Conditions
by: Yuan Xiao, et al.
Published: (2025-01-01) -
A convolutional autoencoder framework for ECG signal analysis
by: Ugo Lomoio, et al.
Published: (2025-01-01) -
An Evaluation of Variational Autoencoder in Credit Card Anomaly Detection
by: Faleh Alshameri, et al.
Published: (2024-09-01) -
Unsupervised Anomaly Detection in Hyperspectral Imaging: Integrating Tensor Robust Principal Component Analysis With Autoencoding Adversarial Networks
by: Atsuya Emoto, et al.
Published: (2025-01-01)