# A Lightweight Convolutional Neural Network Method for Two-Dimensional PhotoPlethysmoGraphy Signals
###### Abstract
Regarding the first category of methods, analyzing waveform characteristics of signals and extracting features from both the time domain and frequency domain has been employed. [PERSON] et al. [8] successfully extracted 40-time domain features from the PPG signal and its first and second derivatives and innovatively proposed a feature sorting algorithm. They further verified the effectiveness of the algorithm through the k-NN classifier. [PERSON] et al. [9] utilized the second derivative of PPG signals, known as APG signals, obtained from ten individual users. The morphology of APG signals was analyzed to extract features, which were then classified using a Bayesian network. Meanwhile, [PERSON] et al. [10] segmented PPG signals based on P-wave intervals and used principal component analysis to extract features successfully utilized for signal classification. In the second category, deep neural network models are employed for automatic feature classification and learning from PPG signals. Deep learning methods can automatically learn useful features directly from raw data without requiring manual feature design and selection. [PERSON] et al. [11] designed and trained a multi-scale feature fusion deep learning (MFFD) model. This model is mainly based on convolutional neural network architecture and is used to effectively extract the features of PPG signals and to learn how to accurately distinguish different individuals based on each person's unique PPG pattern. [PERSON] et al. [12] first proposed PPG enhancement technology to generate multi-scale PPG signals and then proposed a deep end-to-end model to extract and classify multi-scale features. [PERSON] et al. [13] proposed a one-dimensional twin neural network biometric model based on PPG, which reduced noise and retained individual unique characteristics through the multi-period averaging method, achieving efficient and safe identification and authentic cation. [PERSON] et al. [14] used a two-way long short-term memory deep learning algorithm in their research and successfully designed an identity authentication model based on PPG signals. [PERSON] et al. [15] designed a novel deep learning model, CorNET, which combines two convolutional neural network layers and two long short-term memory layers for identity authentication tasks. The network combines two convolutional neural layers and two long short-term memory layers. [PERSON] et al. [16] proposed an end-to-end recognition architecture based on the original PPG signal, mainly built by a convolutional neural network. However, some methods convert PPG signals into two-dimensional image data and utilize deep learning methods for classification. [PERSON] et al. [17] transformed one-dimensional PPG signals into two-dimensional spectrograms and employed convolutional neural networks for automatic feature extraction and classification tasks. Using the scalogram technique, [PERSON] et al. [18] converted one-dimensional PPG signals into two-dimensional images. They developed a CVT-ConvMixer classifier and attention mechanisms to achieve individual identity recognition. The main problem of the first type of method in the above literature is that the features extracted by analyzing the waveform characteristics of the signal or the signal characteristics from the time domain and frequency domain are often not comprehensive enough, and manual processing of features is prone to errors. In the second type of method, the deep learning model used has a relatively complex network structure and many parameters, which results in high computational complexity and consumes many resources. The method in this article uses a neural network model to extract features and learn to classify PPG signals which are automatically converted into two-dimensional images.