Abstract
To design a classification algorithm of heart sounds with low hardware requirements and applicability to mobile terminals, this paper proposes a laconic heart sound neural network (LHSNN). First, we propose three requirements that must be met in the LHSNN design. Then, the specific implementation method of the LHSNN is given as follows: 1) Using a spectrogram as the representation of the heart sound features, the size of the heart sound spectrum is determined according to the principle of lossless information. 2) According to the characteristics of the heart sounds and the design requirements, a neural network is selected and deeply analyzed. 3) Through the optimized method, the network structure satisfies the requirements for running on mobile terminals. Finally, the PhysioNet/CinC Challenge 2016 public heart sound database is used as the experimental object in order to establish a heart sound spectrum library. The experimental results show that the LHSNN can obtain the recognition rate of 96.16% and the modify accuracy of 0.8950, and it can also run on mobile terminals. In addition, the LHSNN has been proven to be adaptable by using the open heart sound dataset of the University of Catania. The research in this paper has positive significance for the classification and recognition of heart sounds in the natural environment.
Highlights
Heart sound classification is not a new topic
DESIGN METHOD According to the design requirements, this paper proposes a laconic neural network model that has a high recognition rate for heart sound signals and can run on mobile terminals such as mobile phones
To theoretically prove the accuracy of the model, we propose two basic conditions that need to be met to design LHSNN: 1) For each convolutional layer of the heart sound signal, it should be ensured that the receptive field contains all the information of a heart sound cycle; 2) The topmost receptive field should be less than or equal to the size of the heart sound spectrum
Summary
Heart sound classification is not a new topic. Many researchers are working on designing a practical heart sound classifier system in order to improve the diagnostic accuracy of heart sounds. Most of them use neural networks [1]–[7], support vector machines [8], [9] and some complex preprocessing steps such as statistical analysis [10] to perform classification tasks. Studies such as literature [4] have adopted a number of processing steps, which show that the researchers can select the best part of the heart sound signal as the input of the system and these programs are not ideal for processing real scenes.
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