A Wearable High Blood Pressure Classification Processor Using Photoplethysmogram Signals through Power Spectral Density Features

High blood pressure (BP) is a major source of health problems related to mental stress, cardiac issues, kidney problems, vision, and brain. High BP bursts can damage and rupture blood vessels and cause strokes. Therefore, it is quite important to continuously monitor it for high BP patients. Conventional BP monitoring devices a) can cause discomfort and b) not suitable for intermittent monitoring. The photoplethysmographic (PPG) signals measure the volume changes in the human blood through human skin. This work presents a high BP classification processor using PPG signals through an artificial intelligence (AI) based boosted circuit. A data set of 25 participants was collected. Ten out of the 25 participants were high blood pressure patients with systolic BP (SBP) and, diastolic BP (DBP) values higher than 140mmHg and 90mmHg, respectively. The AI boosted circuit calculates the power spectral densities, power spectral densities difference, and the sum of the consecutive difference between PPG signals. The features are forwarded to a small 3-level Decision Tree (DT) classifier. The decision tree classifier classifies the high SBP and DBP as high or normal/low with 96.2% classification accuracy. The SBP values
$\geq$
130mmHg and
$<$
130mmHg were classified as HIGH SBP or LOW/NORMAL SBP respectively. Similarly, the DBP values
$\geq$
80mmHg and
$<$
80mmHg were classified as HIGH DBP or LOW/NORMAL DBP, respectively. The system was implemented on an Artix-7 FPGA which consumes power of
$\approx 18.23uW$
@ 50 MHz.