Abstract
Wrist pulse contains important information of human health, so the diagnosis and analysis based on pulse signal is of great significance. In this study, a one-dimensional convolutional neural network (1D CNN) model is proposed to distinguish from pregnancy pulse normal pulse. The performance of the proposed 1D CNN was validated with a collected data set consists of 160 subjects. The 1D CNN proposed with clique blocks style architecture and transition blocks is employed. Furthermore, the three clique blocks go through the pooling layer, and extend the one-dimensional data into vectors through the full connection layer, respectively. By using stacked blocks and transition blocks, the proposed CNN leading a promising classification performance. The F-score, accuracy, precision and recall were used to evaluate the effectiveness of this method in pregnancy pulse detection. The experimental results showed that the proposed 1DCNN has a very high averaged accuracy of \(97.08\%\), which indicated that the method can better used for pulse classification.
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The work is supported by the key project at central government level (Grant No. 2060302).
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Li, N., Jiao, Y., Mao, X., Zhao, Y., Yao, G., Huang, L. (2021). Analysis of Pregnancy Pulse Discrimination Based on Wrist Pulse by 1D CNN. In: Pan, L., Pang, S., Song, T., Gong, F. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2020. Communications in Computer and Information Science, vol 1363. Springer, Singapore. https://doi.org/10.1007/978-981-16-1354-8_23
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DOI: https://doi.org/10.1007/978-981-16-1354-8_23
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