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

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.

Automated summary

Accurately classifying dead embryos and live embryos is crucial for the successful development of vaccines. In this study, we utilized deep learning techniques and photoplethysmographic waveform to detect embryo activity. By rescaling the data and constructing a novel detection model, we achieved equal treatment of each feature in the data and powerful feature extraction capabilities.