Design and Application of a Laconic Heart Sound Neural Network

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.