Patient signal feature extraction technology for intelligent nursing bed

The signal of intelligent nursing bed is easily polluted by noise during the acquisition process, so it is necessary to study the noise reduction processing algorithm of the signal. This paper uses deep learning to optimise the bowel sound feature extractor, takes the edge computing system with GPU configuration as the implementation object, and proposes a pre-defecation prediction based on Mobilenet-RF. This paper proposes to use the random forest algorithm to classify the bowel sound signal features extracted by Mobilenet to achieve early classification and prediction of patient characteristics. Furthermore, this paper uses bowel sound signal processing and pre-defecation prediction as cases for experimental analysis. The experimental results show that the Mobilenet-RF algorithm proposed in this paper achieves the highest accuracy of 95.68%. Then, this paper verifies the generalisation ability of the model through multi-dataset experiments, proving the superiority of using random forest for classification.