Heart sound classification based on scaled spectrogram and partial least squares regression

Phonocardiogram (PCG) signal analysis is an effective and convenient method for the preliminary diagnosis of heart disease. In this study, a scaled spectrogram and partial least squares regression (PLSR) based method was proposed for the classification of PCG signals. Proposed method is mainly comprised of four stages, namely as being heart cycle estimation, spectrogram scaling, dimension reduction and classification. At the heart cycle estimation stage, the short time average magnitude difference of the Shannon energy envelope is applied. Then the spectrogram of the obtained heart cycle is calculated for feature extraction. However, the sizes of the spectrograms between different PCG signals are usually not the same. In order to overcome the difficulty of direct comparison, the bilinear interpolation is used for the spectrogram to get the scaled spectrogram with a fixed size. Nevertheless, the scaled spectrogram contains a large quantity of redundant and irrelevant information. To extract the most relevant features from the scaled spectrogram, we adopt the PLSR to reduce the dimension of the scaled spectrograms. Since PLSR has the advantage of using the category information during the dimension reduction process, the extracted features are more discriminative. Then the classification results are obtained via support vector machine (SVM). The proposed method is evaluated on two public datasets offered by the PASCAL classifying heart sounds challenge, and the results are compared to those obtained using the best methods in the challenge, thereby proving the effectiveness of our method.