ECG Identification Algorithm Based on Improved MobileNet
Information security is becoming more and more important in society today.The identification technology based on the electrocardiogram(ECG)signal presents its unique advantages,due to its outstanding anti-counterfeiting performance on"living"detection.To achieve more efficient identification in mobile scenarios,a depth migration recognition model SP-MobileNet based on sparse convolution and lightweight network MobileNet is proposed.Firstly,the original ECG signal is pre-processed:denoised by wavelet soft threshold,blindly segmented into signal segments,and transformed into ECG time-frequency map as the input of the network by generalized S transform.Then,an ECG recognition model based on SP-MobileNet is built:a sparse convolution calculation strategy is adopted in the convolutional layers of MobileNet,along with transfer learning method,realizing the seamless connection from the large-sample ECG data training collected from Lead Ⅱ to the small-sample ECG recognition collected by fingertips.Experimental results indicate that the proposed algorithm can perform ECG identification efficiently and quickly,achieving a recognition accuracy of 98.00％and an inference speed of 50.4 FPS on the PhysioNet/Cinc Challenge 2017 dataset.
NETL
NSTL
万方数据
