Novel three kernelled binary pattern feature extractor based automated PCG sound classification method

Abstract

Background: Heart valve diseases are commonly seen ailments, and many people suffer from these diseases. Therefore, early diagnosis and accurate treatment are crucial for these disorders. This research aims to diagnose heart valve diseases automatically by employing a new stable feature generation method. Materials and method: This research presents a stable feature generator-based automated heart diseases diagnosis model. This model uses three primary sections. They are stable feature generation using the improved one-dimensional binary pattern (IBP), selecting the most discriminative feature with neighborhood component analysis (NCA), and classification employing the conventional classifiers. IBP uses three kernels, and they are named signum, left signed, and right signed kernels. By applying these kernels, 768 features are generated. NCA aims to choose the most discriminative ones, and 64 features are chosen to employ NCA. The k nearest neighbor (kNN) and support vector machine (SVM) classifier are employed in the classification phase. Open access (public published) Phonocardiogram signal (PCG) sound dataset is used to calculate this model's measurements. This dataset contains 1000 PCGs with five categories. Results: The presented IBP and NCA-based heart valve disorders classification model tested using kNN and SVM classifier and attained 99.5% and 98.30% accuracies, respectively. Conclusions: Per the results, the presented IBP and NCA-based PCG sound classification is a successful method. Moreover, this model is basic and high accurate. Therefore, it is ready for the development of real-time implementations. (C) 2021 Elsevier Ltd. All rights reserved.