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...

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Vydané v:	Frontiers in neuroinformatics Ročník 19; s. 1530047
Hlavní autori:	Zeng, Peipei, Kang, Shuimiao, Fan, Fan, Liu, Jiyuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Research Foundation 29.01.2025 Frontiers Media S.A
Predmet:	Accuracy Artificial intelligence Auscultation Cardiovascular disease Classification Congenital diseases convolutional autoencoder Deep learning Heart Heart diseases heart sound detection Medical screening Neural networks Neuroscience Noise Noise reduction one classification support vector machine sample imbalance semi-supervised anomaly detection Sound one classification support vector machine sample imbalance semi-supervised anomaly detection convolutional autoencoder heart sound detection
ISSN:	1662-5196, 1662-5196
On-line prístup:	Získať plný text
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