Design of Smart Brain Oxygenation Monitoring System for Estimating Cardiovascular Disease Severity

Abstract

Clinically, cardiovascular disease (CVD) patients need physicians to suggest different exercise training and rehabilitation procedures to improve their cardiopulmonary function (CPF). In previous studies, several approaches, such as cardiopulmonary exercise testing (CPET), echocardiography and computed tomography angiography (CTA), were proposed to indirectly estimate the rehabilitation effect on CPF. However, the above approached require experienced operators and complex equipment. In this study, a smart and wearable brain oxygenation monitoring system without motion artifact and crosstalk is proposed to estimate the blood circulation state of brain tissue directly during incremental exercise. Moreover, the technique of neural network is also used for classifying different CPF groups from the indexes extracted from the measured hemoglobin parameters. The experimental results show that the defined indexes extracted from the hemoglobin parameters can present the state of CPF, and the proposed smart brain oxygenation monitoring system can also effectively and automatically classify different CPF groups from these indexes via artificial intelligence. The proposed system therefore may assist physicians in the clinical evaluation of the CVD severity and rehabilitation effect on CPF in the future.