Effectively Measuring Respiratory Flow with Portable Pressure Data using Back Propagation Neural Network

Continuous respiratory monitoring is an important tool for clinical monitoring. The most widely used flow measure device is nasal cannulae connected to a pressure transducer. However, most of these devices are not easy to carry and continue working in uncontrolled environments is also a problem. For portable breathing equipment, due to the volume limit, the pressure signals acquired by using the airway tube may be too weak and contain some noisy, leading to huge errors in respiratory flow measures. In this paper, a cost-effective portable pressure sensor based respiratory measure device is designed. This device has a new airway tube design, which enables the pressure drop efficiently after the air flowing through the airway tube. Also, a new back propagation (BP) neural network based algorithm is proposed to stablise the device calibration and remove pressure signal nosie. For improving the reability and accuracy of proposed respiratory device, a through experimental evaluation and a case study of proposed BP neural network algorithm have been carried out. The results show that giving proper parameters setting, the proposed BP neural network algorithm is capable of efficiently improving the reliability of new designed respiratory device