Heart sound classification with signal instant energy and stacked autoencoder network
Graphical abstract
Introduction
The most common method used by doctors to diagnose heart diseases is auscultation. Auscultation means listening to heart sounds with a stethoscope. Regular or irregular rhythms of heart sounds provide important information about heart diseases such as pathological cardiac status, valve diseases, and heart failure. Therefore, the heart sounds are of a great importance in the early diagnosis of heart diseases [[1], [2], [3]].
In fact, the heart sounds have four main components: S1, S2, S3, and S4 that repeat at regular intervals. However, the two main components of the heart sound are S1-Systole and S2-Diastole, and the sounds except these are mostly inaudible during auscultation. Fig. 1 gives an example of normal, murmur and extrasystole heart sounds in the dataset used in this study and S1-S2 sounds. Also abnormalities are shown on the figure. In studies, it has often been emphasized that these components must be well defined in order to easily and accurately diagnose heart diseases. Therefore, in many studies, heart sound signals were preprocessed and segmented into S1-S2 sounds by different methods, before classification. In such studies, the first step for automatic diagnosis of heart diseases from heart sounds is usually segmentation of heart sounds. Correct separation of systolic and diastolic regions is very important in the correct detection of cardiac sound components. Thus, pathological conditions in a heart sound can be obviously separated [4].
The aim of this study is to show that direct classification can be made easily with instant signal energy data, without segmentation by using more complex methods or needing feature selection with hybrid methods. It is also to discuss the obtained results by comparing with other examples. In this context, an exemplary study was performed in which the instant energies of the heart sounds calculated using the resampled energy method proposed by Deperlioglu [1] were classified with stacked autoencoder network. The rest of the article was organized as follows. In the second part, related works that reveal the purpose of the article were given. In the third section, a brief information about the data set and used methods were given. In the fourth section, results and comparative evaluations were made. In the last section, the contributions of the proposed method are presented.
Section snippets
Related works
As mentioned before, the majority of research on heart sounds has been conducted to improve classification success. Segmentation of S1 and S2 sounds in heart sounds, determination of peak values of S1 and S2 sounds, the use of different filtering methods and the use of different classification techniques are the most preferred ways to increase the classification success. Some case studies are given in Table 1.
In his study, Deperlioglu segmented S1-S2 sounds using the resampled energy method and
Materials and method
As can be seen from the examples above, signal energy is often used as a support method for segmentation. The aim of this study is to discuss the results obtained by demonstrating that direct classification can be done easily with signal energy data without the need of segmentation or more complex methods and algorithms. For this, the resampled energy method proposed by Deperlioglu was used [1]. For evaluation of proposed method, the classification was made with PASCAL B-training heart sounds
Results and discussion
The most appropriate SAEN configuration was obtained by making use of previous experiences for classification application. In the classification application of the signal energy data, 80% samples were used as a training data and 20% samples were used as a test data from PASCAL B-training data set. The maximum number of a learning epochs was 1000. Each classification study was repeated 20 times for different the training and test dataset.
In 20 trials, the lowest overall classification accuracy
Conclusion
In this study, it was found that the classification accuracy rate was almost same with the classification of the segmented data. From the obtained results, it is seen that the instant energy values could be classified directly as the segmented data or features. In addition, considering the energy values of heart sound data in the same dataset classified directly by ANN in the reference 4, it is seen that the SAEN classification is more successful. It is understood that the energies of the heart
CRediT authorship contribution statement
Omer Deperlioglu: Methodology.
Declaration of Competing Interest
We certify that we have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this
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