Similarity-Based Signal Embedding for Heart Sound Abnormality Detection

Abstract

Heart sound (HS) analysis is important for early detection of cardiovascular abnormalities. Although deep learning methods have shown strong performance, they often require large datasets and high-dimensional inputs. In this study, a similarity-based framework is proposed for heart sound abnormality detection using low-dimensional statistical features. Experiments were conducted on the publicly available HLS-CMDS dataset, and only heart sound recordings were used. Each signal was divided into five segments, and time- and frequency-domain statistical features were extracted. Instead of direct classification, a pairwise similarity learning approach was adopted. Feature-difference vectors were constructed between segment pairs, and an RBF-kernel SVM was trained to learn similarity relationships. The learned similarity function was then used to generate a prototype-based embedding for each signal. The proposed method provides a computationally efficient and interpretable alternative to deep learning models, especially for small-scale biomedical datasets.

Keywords

• Anomaly detection
• Embedding learning
• Heart sound analysis
• Low-dimensional features
• Similarity learning
• Support vector machines (SVM)

Kalp Sesi Anomali Tespiti için Benzerlik Tabanlı Temsil Yaklaşımı

Abstract

Kalp sesi (KS) analizi, kardiyovasküler anormalliklerin erken tespitinde önemli bir rol oynamaktadır. Derin öğrenme yöntemleri yüksek performans göstermesine rağmen, genellikle büyük veri kümeleri ve yüksek boyutlu girdiler gerektirmektedir. Bu çalışmada, düşük boyutlu istatistiksel özellikler kullanılarak kalp sesi anormalliklerinin tespiti için benzerlik tabanlı bir çerçeve önerilmektedir. Deneyler, herkese açık HLS-CMDS veri seti üzerinde gerçekleştirilmiş ve yalnızca kalp sesi kayıtları kullanılmıştır. Her bir sinyal beş segmente ayrılmış; zaman ve frekans alanına ait istatistiksel özellikler çıkarılmıştır. Doğrudan sınıflandırma yerine, ikili benzerlik öğrenme yaklaşımı benimsenmiştir. Segment çiftleri arasında özellik farkı vektörleri oluşturulmuş ve benzerlik ilişkilerini öğrenmek üzere RBF çekirdekli bir Destek Vektör Makinesi (SVM) eğitilmiştir. Öğrenilen benzerlik fonksiyonu, her sinyal için prototip tabanlı bir temsil uzayı oluşturmak amacıyla kullanılmıştır. Önerilen yöntem, özellikle küçük ölçekli biyomedikal veri kümeleri için derin öğrenme modellerine kıyasla hesaplama açısından verimli ve yorumlanabilir bir alternatif sunmaktadır.

Keywords

• Anomali tespiti
• Temsil öğrenme
• Kalp sesi analizi
• Düşük boyutlu özellikler
• Benzerlik öğrenme
• Destek vektör makineleri (SVM)

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