In some remote mountainous areas of Yunnan,the network signal is weak or no signals even.To implement heart sound classification algorithms on portable devices and meet the offline and mobile needs,a heart sound classification method that could be deployed on the APSOC platform was proposed.The feature extraction of heart sound signals was implemented in the PS section,and convolutional and pooling layers of CNN were implemented in the PL section.Multi-channel parallel and pipeline methods were used to achieve hardware acceleration of the system.Experimental results show that compared to general-purpose CPUs,it achieves 8.91 times hardware acceleration with only 2％classification accuracy lost.Experimental results indicate that the proposed scheme has practical value for assisting in the diagnosis of heart sounds.
关键词
全可编程片上系统/心音分类/先天性心脏病/硬件加速/卷积神经网络/梅尔频率倒谱系数/并行计算
Key words
APSOC/classification of heart sounds/congenital heart diseases/hardware acceleration/convolution neural net-work/Mel frequency cepstral coefficient/parallel computing
维普
万方数据