Classification of heart sound signal using curve fitting and fractal dimension

- Cardiovascular disease is one of the major causes of mortality worldwide.
- Traditionally auscultation is a noninvasive, cheap and simple method for diagnosing diseases.
- Segmentation step was eliminated because a lot of its problems.
- The information of PCG signal sequence is obtained by using the curve fitting method.
- PCG signal is self-similar and crumpled in murmur's location so considered as fractal.

Cardiovascular disease is one of the major causes of mortality worldwide. Audio signal produced by the mechanical activity of heart provides useful information about the heart valves operation. To increase discriminability between heart sound signals of different normal and abnormal persons, extraction of appropriate features is so important. An accurate segmentation of heart sound signal requires its corresponding ECG1 signal. But, acquiring of ECG is generally expensive and time consuming. So, one of the main goals of this paper is to eliminate the segmentation step. In this paper, two feature extraction methods are proposed. In the first proposed method, curve fitting is used to achieve the information contained in the sequence of heart sound signal. In the second method, the powerful features extracted by MFCC2 are fused with the fractal features by stacking. The experiments are done on six popular datasets to assess the efficiency of different methods One of the data sets contains four classes and the rest of them include two classes (normal and pathologic). In the classification step, the nearest neighbor classifier with Euclidean distance is used. The proposed method has good performance compared to previous methods such as Filter banks and Wavelet transform. Particularly, the performance of the second method is significantly better than the first proposed method. For three data sets, the overall accuracy of 92%, 81% and 98% are achieved, respectively.