Deep learning based medical gas identifying method

Abstract

The invention discloses a deep learning based medical gas identifying method. The deep learning based medical gas identifying method specifically includes utilizing original frequency response signals to perform simple uniformization, inputting shed typed own coding network, and extracting layer by layer to learn abstract characteristics of original data. The whole network shield the abstract characteristics, dimensionality reduction, drifting restraining and the like to the outside environment, and a classifying layer is added in the network, so that the characteristics can be classified into a classifier to classify. The training process is classified into a pre training step and a micro adjusting step, network learning ability can be effectively increased, and new samples are input into the network to directly acquire predicted category after the training is finished. With the deep learning based medical gas identifying method, effective identifying characteristics of medical gas can be automatically abstracted, steps such as characteristics abstraction, characteristics selection and drifting restraining are combined, complexity in traditional methods is greatly simplified, and gas detecting and identifying efficiency is increased.

Description

technical field [0001] The invention belongs to the technical field of biomedicine, and in particular relates to a medical gas identification method. Background technique [0002] Machine olfaction is an artificial intelligence system. Its basic principle is: odor molecules are adsorbed by sensor arrays to generate electrical signals, and then use various signal processing techniques to extract features, and then make judgments through computer pattern recognition systems to complete gas identification and concentration. Measurement etc. The electronic nose system is a typical application of machine olfaction, which plays a very important role in the medical field, such as diagnosing certain diseases, identifying bacterial species in blood, and detecting gases harmful to the respiratory system. [0003] Sensing gas detection and identification has important applications in the medical field. For example, electronic nose equipment can be used to collect sample data in the mo...

AI Technical Summary

Problems solved by technology

[0015] However, these methods all assume that the drift law of the sensor is linear, which has not been confirmed, and often requires a reference gas whose chemical properties are relatively stable over time and highly similar to the sensor behavior of the gas to be analyzed. It is undoubtedly quite harsh in practical applications.

In addition, these methods are quite complicated to operate in practical applications, and the efficiency is very low

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