Hypertension is a medical condition characterized by high blood pressure, and classifying it into its various stages is crucial to managing the disease. In this project, a novel method is proposed for classifying stages of hypertension using Photoplethysmography (PPG) signals and deep learning models, namely AvgPool_VGG-16. The PPG signal is a non-invasive method of measuring blood pressure through the use of light sensors that measure the changes in blood volume in the microvasculature of tissues. PPG images from the publicly available blood pressure classification dataset were used to train the model. Multiclass classification for various PPG stages were done. The results show the proposed method achieves high accuracy in classifying hypertension stages, demonstrating the potential of PPG signals and deep learning models in hypertension diagnosis and management.

翻译：高血压是一种以血压升高为特征的医疗状况，将其分为不同阶段对于疾病管理至关重要。本研究提出了一种利用光电容积脉搏波（PPG）信号和深度学习模型（即AvgPool_VGG-16）对高血压阶段进行分类的新方法。PPG信号通过使用光传感器测量组织微血管中血容量变化，是一种无创的血压测量方法。实验采用公开的血压分类数据集中的PPG图像对模型进行训练，并对不同PPG阶段进行了多分类处理。结果表明，该方法在高血压阶段分类中实现了高准确性，展示了PPG信号与深度学习模型在高血压诊断与管理中的潜力。