Year: 2026
Type: article
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.
Authors: Abdullah Çalışkan, Zeynep Sude Kuş
Institution: Kahramanmaraş Sütçü İmam University
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7171522345