Cuff-Less Non-Invasive Blood Pressure Measurement Using Various Machine Learning Regression Techniques and Analysis

This paper proposes a new approach for non-invasive cuff-less arterial Blood Pressure (BP) estimation using pulse transit time (PTT). The ECG and PPG signals were acquired at a sampling rate of 500Hz. Standard cuff based Sphygmomanometer used to take reference BP and heart rate simultaneously. The hardware for the acquiring the ECG and PPG signals were designed and fabricated and were made and study was carried out with 60 subject during various activities. The objective of this work is to estimate the Systolic BP and Diastolic BP using PTT techniques and to apply regression analysis using machine learning methods for estimating the BP, compare the results with recording simultaneously carried out using the standard devices. The proposed work concludes that AdaBoost algorithm has highest accuracy in estimating systolic and diastolic BP values. The readings obtained are in accordance with the AHA standards and are in acceptable limits and can be used for measuring BP in wearable devices.