Key result
A smart brain oxygenation monitoring system using near-infrared spectroscopy and a neural network significantly differentiated patients with better versus poorer cardiopulmonary function (RBFNN output 0.504 vs 0.296, p=1.566E-4).
Why the study?
Existing approaches to estimate cardiopulmonary function rehabilitation effects in cardiovascular disease require experienced operators and complex equipment, prompting the need for a smart, wearable monitoring system.
Does a smart wearable brain oxygenation monitoring system accurately classify cardiopulmonary function in patients with cardiovascular disease?
Cross-Sectional (n=49)
No
Does a smart wearable brain oxygenation monitoring system accurately classify cardiopulmonary function in patients with cardiovascular disease?
Absolute Event Rate: 0.504% vs 0.296%
p-value: p=1.566E-4
A novel wearable brain oxygenation monitoring system using neural networks can classify cardiopulmonary function during exercise, potentially aiding in the clinical evaluation of cardiovascular disease severity.
May support noninvasive CPF monitoring via wearable oximetry in CVD; leaves open prospective validation against CPET before clinical use.
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.
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Chou et al. (2020) conducted a cross-sectional in Cardiovascular disease (n=49). Smart brain oxygenation monitoring system (NIRS) vs. Poorer cardiopulmonary function group (peak METs < 5) was evaluated on Radial basis function neural network (RBFNN) output value for classifying cardiopulmonary function (p=1.566E-4). A smart brain oxygenation monitoring system using near-infrared spectroscopy and a neural network significantly differentiated patients with better versus poorer cardiopulmonary function (RBFNN output 0.504 vs 0.296, p=1.566E-4).
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