Abstract:
Photoplethysmogram is an optically obtained signal working based on the
volumetric change of blood. As heart diseases are correlated with the pumping
of blood, PPG can be studied for detecting cardiovascular diseases. Researchers
have already analyzed PPG signals for various disease detection, including
hypertension, coronary artery disease, diabetes, and others. Also, two important
health parameters: heart rate and blood pressure have been predicted from PPG
signals in several studies. However, most of the work has been done at the
software level without any hardware implementation. In addition, two
important cardiovascular diseases related to blood flow in the brain: cerebral
infarction and cerebrovascular disease are yet to be explored based on PPG
signal. Hence, this study aims to develop a hardware-based system that can
detect several cardiovascular diseases - hypertension, cerebral infarction,
cerebrovascular disease, diabetes, and a few combinations of them. The study
checks the feasibility of detecting these diseases individually in a binary
classification system and also in a multiclass classification system. A system is
also implemented for predicting heart rate and blood pressure from PPG signals.
The systems are developed in Xilinx system generator targeting Zedboard zynq
7000 and zynq ultrascale+ FPGA board. The binary classification system uses 11
features and applied SVM classifier to get the accuracy of 96.37%, 93.48%,
96.43%, and 88.46% for detecting hypertension, cerebral infarction,
cerebrovascular disease, and diabetes, respectively, consuming a total of 0.693 W
power. The multi-class classification system utilizes a total of 1.403 W of power,
providing an accuracy of 79.83% for detecting 7 classes of diseases. Also, the
heart rate and blood pressure estimation system utilizes 0.353 W of power. The
heart rate is predicted with 4.04% error while systolic and diastolic blood
pressure are estimated with 3.77% and 4.8% error, respectively. The designed
prototype can be further extended to develop wearable devices, and
smartwatches and can be useful for medical treatment and analysis.
