Diagnosis analysis of rectal function through using ensemble empirical mode decomposition-deep belief networks algorithm.

Abstract

The rectal motility function can reflect a person's rectal health status. To diagnose the rectal motility function after artificial anal sphincter implantation, this paper proposes a rectal function diagnosis model based on ensemble empirical mode decomposition-deep belief networks (EEMD-DBNs). Because of the rectal pressure signals that are unstable and subjected to noise interferences, an EEMD framework based on EMD, which can reduce the effect of signal modal mixing, is proposed. EMD and EEMD were used to decompose the analog signal, respectively, and it was found that EEMD can significantly reduce the effect of mode aliasing. During the rectal pressure signal decomposition experiment, by analyzing the intrinsic mode functions generated by the signals from normal people and diseased patients, the rectal signals at these two different conditions can be well distinguished. Additionally, the DBN was introduced to perform deep learning to extract the multi-dimensional features of rectal signals and then output the classification results via using the top-level classifier, which can overcome the difficulties in extracting the rectal signal features. The results showed that, following the principle of balancing the diagnosis accuracy and model running speed, the best diagnosis performance was achieved when three restricted Boltzmann machines and five layers of DBN model were set, with the diagnosis rate of 85%. The diagnostic model used in this study can distinguish the signals between normal and abnormal rectal functions with accurate performance, thus providing the technical support for the recovery of the rectal motility function of artificial anal sphincter implanters.