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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Veröffentlicht in:Frontiers in neuroinformatics Jg. 19; S. 1530047
Hauptverfasser: Zeng, Peipei, Kang, Shuimiao, Fan, Fan, Liu, Jiyuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Research Foundation 29.01.2025
Frontiers Media S.A
Schlagworte:
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
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Zusammenfassung: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 disease. Due to the low time and high efficiency, most work focuses on the semi- supervised anomaly detection method. However, the anomaly detection effect of this method is not high because of massive data with uneven samples and different noise. To improve the accuracy of anomaly detection under unbalanced sample conditions, we propose a new semi-supervised anomaly detection method (WCOS) based on semi-supervised clustering, which combines wavelet reconstruction, convolutional autoencoder, and one classification support vector machine. In this way, we can not only distinguish a small proportion of abnormal heart sounds in the huge data scale but also filter the noise through the noise reduction network, thus significantly improving the detection accuracy. In addition, we evaluated our method using real datasets. When the noise of sigma = 0.5, the AUC standard deviation of the WR-CAE-OCSVM is 19.2, 54.1, and 29.8% lower than that of WR-OCSVM, CAE-OCSVM and OCSVM, respectively. The results confirmed the higher accuracy of anomaly detection in WCOS compared to other state-of-the-art methods.
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Reviewed by: Anoop Tiwari, Central University of Haryana, India
Subodh Kumar, Central University of Rajasthan, India
Edited by: Pritpal Singh, Central University of Rajasthan, India
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2025.1530047