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Identification of pregnancy in women based on fingertip pulse using a multi-feature fusion neural network model
Zhuya Huang1, Junsheng Yu1,2,3, Ying Shan1
1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
This study proposes a rapid method for determining pregnancy status based on fingertip pulse signals. A finger pulse sensor collects data, which is processed into unified multimodal signals. The Bamboo-Net model, combining ResNet, LSTM, and 1D-CNN, extracts key features from time, frequency, and time-frequency domains. Tested on 346 training and 138 testing samples, the model achieves 91% accuracy with 6 s input, outperforming mainstream methods. Recognition rates for mid and late pregnancy are higher than for early pregnancy, highlighting its potential for practical applications.
This study proposes a rapid method for determining pregnancy status based on fingertip pulse signals. A finger pulse sensor collects data, which is processed into unified multimodal signals. The Bamboo-Net model, combining ResNet, LSTM, and 1D-CNN, extracts key features from time, frequency, and time-frequency domains. Tested on 346 training and 138 testing samples, the model achieves 91% accuracy with 6 s input, outperforming mainstream methods. Recognition rates for mid and late pregnancy are higher than for early pregnancy, highlighting its potential for practical applications.
