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Photoplethysmographic waveform detection for determining hatching egg activity via deep neural network

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Abstract

It is essential to classify dead embryos and live embryos accurately in developing a successful vaccine. The deep learning-based classification of heartbeat signals to determine embryo activity is considered to be the most effective, but generally speaking, existing detection methods are either harmful to embryos or inefficient. The photoplethysmographic (PPG) waveform was used in this study for embryo activity detection. The PPG technique is non-invasive and works based on detection of optical absorption intensity in the blood. We rescaled the original data to weight each feature equally, which allows the CNN model to treat every feature in the data equally without neglecting low-intensity features. We also constructed a novel detection model capable of powerful feature extraction. Our model is based on the CNN structure and GRU. The CNN structure is the basic feature extractor. We added a channel attention mechanism to recalibrate the feature map channel, which enhances the network’s ability to extract useful features. The GRU module captures timing characteristics to compensate for the inability of the CNN to extract temporal information. We validated our approach on experimental data to find that it outperforms several baseline methods.

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Acknowledgements

This work was supported by the Program for Innovative Research Team in University of Tianjin (Grant No. TD13-5034), and the Natural Science Foundation of Tianjin City (Grant No. 18JCYBJC15300).

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Authors and Affiliations

  1. School of Life Sciences, Tiangong University, Tianjin, 300387, China

    Lei Geng & Zhitao Xiao

  2. School of Electronics and Information Engineering, Tiangong University, Tianjin, 300387, China

    Quan Guo

  3. Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin, 300387, China

    Lei Geng, Quan Guo & Zhitao Xiao

  4. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, 2522, Australia

    Jun Tong

  5. School of Economics and Management, Tiangong University, Tianjin, 300387, China

    Yuelong Li

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  1. Lei Geng
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Correspondence to Zhitao Xiao.

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Geng, L., Guo, Q., Xiao, Z. et al. Photoplethysmographic waveform detection for determining hatching egg activity via deep neural network. SIViP 16, 955–963 (2022). https://doi.org/10.1007/s11760-021-02040-y

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  • DOI: https://doi.org/10.1007/s11760-021-02040-y

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