Abstract
A normal and abnormal heart sound identification method was put forward in the paper. The wavelet packet energy features of the heart sounds were extracted and LM-BP neural network was used as the classifier. Experimental results showed that the proposed algorithm converged much faster than traditional BP neural network, and achieved better results compared with two traditional heart sound processing methods based on STFT and Spectrogram analysis.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61601081, 61471081; Fundamental Research Funds for the Central Universities under Grant Nos. DC201501056, DCPY2016008, DUT15QY60, DUT16QY13; Dalian Youth Technology Star Project Supporting Plan under Grant No. 2015R091.
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Li, T., Tang, H., Xu, Xk. (2017). Identification of the Normal and Abnormal Heart Sounds Based on Energy Features and Neural Network. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_60
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DOI: https://doi.org/10.1007/978-3-319-69923-3_60
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