In order to solve the problem of existing diagnostic methods for chronic gastritis which are complex and traumatic, a novel noninvasive method for diagnosis of chronic gastric based on e-nose and deep convolutional neural network is proposed. Firstly, in order to collect samples, a respiratory gas sampling device was established and the response curve of respiratory gas is generated. Then, a deep convolutional neural network for the diagnosis of chronic gastritis is proposed to recognize and classify the respiratory gas response curve. The DCNN model attained good results with accuracy, sensitivity, and specificity of 85.00%, 90.00%, and 80.00%, respectively, for chronic gastric prediction. The proposed method provides a new way for the clinical auxiliary diagnoses of chronic gastric.

Dysphagia is a common complication of cerebrovascular accidents, and greatly affects the prognosis of patients. To meet the portability requirement for instruments used in the diagnosis of dysphagia in clinical settings, a novel sensor for measuring tongue pressure is designed to help doctors evaluate the severity of dysphagia. A sensor with a special beam structure converts the deformation of strain gauges to analog voltage signals through a Wheatstone bridge, then translates the result to a value for tongue pressure. The relationship between the tongue pressure and voltage is fitted by a least squares method and also by using an extreme learning machine. The two sets of results are compared and the superiority of using the extreme learning machine is verified. For the designed tongue-pressure sensor with an outer diameter of 10 mm and a thickness of 3 mm, the detection range is from 0 to 15 kPa, and the relative error of measurement can be controlled within 0.91%.