Various embodiments of the disclosed invention provide a combination of feature-based techniques and deep learning techniques for distinguishing between normal and abnormal heart sounds. A feature-based classifier (60) is applied to the phonocardiogram (PCG) signal to obtain a feature-based anomaly classification of the heart sound represented by the PCG signal, and a deep learning classifier (70) is also applied to the PCG signal. Obtain a deep learning anomaly classification of heart sounds represented by PCG signals. A final decision joint analyzer (80) is applied to the feature-based anomaly classification of the heart sounds represented by the PCG signal and the deep learning anomaly classification to determine the final anomaly classification determination of the PCG signal.本開示の発明のさまざまな実施形態は、正常な心音と異常な心音の間の区別をするための特徴ベースの手法と深層学習手法の組み合わせを提供する。特徴ベースの分類器（６０）が心音図（PCG）信号に適用され、PCG信号によって表わされる心音の特徴ベースの異常性分類を得て、深層学習分類器（７０）もPCG信号に適用されて、PCG信号によって表わされる心音の深層学習異常性分類を得る。PCG信号によって表わされる心音の前記特徴ベースの異常性分類および前記深層学習異常性分類に最終判断合同分析器（８０）が適用されて、PCG信号の最終的な異常性分類判断を決定する。
