Towards Photoplethysmogram Based Non-Invasive Blood Pressure Classification

Abstract

A novel blood pressure classification model using Phototplethysmogram (PPG) is proposed in this work. The proposed model uses signal processing and machine learning algorithms to classify blood pressure in four stages: normal, elevated, stage 1 and stage 2. A total of 83 features were extracted from the PPG signal which includes 71 statistical features and 12 characteristic features. We have used random forest classifier to train and test our predictive model. The proposed method is evaluated on publicly available MIMIC database for 20 different individuals. The database contains raw PPG data for different users and Arterial Blood Pressure (ABP) to calculate the systolic and diastolic blood pressure to be used as the ground truth for training and validation purposes. We have achieved an overall accuracy of 90.8% over the four classes of blood pressure levels. The results indicate that the proposed model will be ideal for integration into a non-invasive blood pressure monitoring system with significant accuracy.