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:高血压是一种以高血压为特征的医疗状况,将其分类为各个阶段对于管理疾病至关重要。该项目提出了一种新的方法,使用光谱体积描记法(PPG)信号和深度学习模型(即AvgPool_VGG-16)来分类高血压的阶段。 PPG信号是一种非侵入性的测量血压的方法,通过使用测量组织微血管中血容量的光传感器来实现。使用公开可用的血压分类数据集中的PPG图像来训练模型并进行各种PPG阶段的多类分类。结果显示,所提出的方法在分类高血压阶段方面达到了高精度,证明了PPG信号和深度学习模型在高血压诊断和管理方面的潜力。