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Rehabilitation prediction method and system of deaf patient after CI operation based on machine learning

A technology of machine learning and prediction methods, applied in sensors, medical science, diagnostic recording/measurement, etc., can solve problems such as difficulties in exploring working mechanisms

Inactive Publication Date: 2017-10-24
SUN YAT SEN MEMORIAL HOSPITAL SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In terms of research methods, due to the complexity of brain work, it is more difficult to explore its working mechanism

Method used

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  • Rehabilitation prediction method and system of deaf patient after CI operation based on machine learning
  • Rehabilitation prediction method and system of deaf patient after CI operation based on machine learning
  • Rehabilitation prediction method and system of deaf patient after CI operation based on machine learning

Examples

Experimental program
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no. 1 example

[0067] In the first embodiment, a method for predicting postoperative rehabilitation of deaf patients based on machine learning after CI is characterized in that the prediction method includes:

[0068] Obtain the EEG signals of several congenitally deaf patients, and preprocess the obtained EEG signals;

[0069] Extracting the feature quantity of the EEG signal from the preprocessed EEG signal;

[0070] Perform normalized preprocessing on all EEG signal data;

[0071] Choose a kernel function;

[0072] From the EEG signal data, select a training set and a test set;

[0073] Load the training set and test set into the support vector machine for rehabilitation prediction.

[0074] Further, the method for extracting the feature quantity of the EEG signal from the preprocessed EEG signal includes:

[0075] Obtain the source location result;

[0076] Perform T check;

[0077] The Kendall coefficient correlation test was performed.

[0078] Specifically, the method for obtai...

no. 2 example

[0096] The second embodiment, a machine learning-based rehabilitation prediction system for deaf patients after CI, is characterized in that the system includes:

[0097] The signal acquisition part is used to collect EEG signals and preprocess the EEG signals;

[0098] The signal processing part is used to process the preprocessed EEG signal, and send the processed result to the support vector machine;

[0099] A support vector machine is used to load the processed results for rehabilitation prediction.

[0100] Further, the signal acquisition part includes:

[0101] An EEG signal collection device, used for collecting EEG signals;

[0102] The first memory is used to store the collected EEG signals;

[0103] a first signal processor, configured to preprocess the collected EEG signals;

[0104] The first communication unit is used for communicating with the signal processing part.

[0105] Further, the signal processing part includes:

[0106] The second communication u...

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Abstract

The embodiment of the invention provides a rehabilitation prediction method and system of a deaf patient after a CI operation based on machine learning, and relates to the field of computer signal processing. The method comprises the steps of obtaining multiple electroencephalogram signals of the patient with congenital deafness, and pre-processing the obtained electroencephalogram signals; extracting characteristic quantities of the electroencephalogram signals from the preprocessed electroencephalogram signals; conducting normalization preprocessing on all electroencephalogram signal data; selecting a kernel function; selecting a training set and a testing set from the electroencephalogram signal data; loading the training set and the testing set into a support vector machine, and conducting rehabilitation prediction. The rehabilitation prediction method and system of the deaf patient after the CI operation based on machine learning have the advantages of being high in recognition rate, accuracy rate, practicability and the like.

Description

technical field [0001] The present invention relates to the technical field of computer signal processing, in particular to a method and system for predicting postoperative rehabilitation of deaf patients after CI based on machine learning. Background technique [0002] Deafness is a common clinical genetic disease, which will have a very serious impact on people's life and health. According to statistics, 1 to 3 deaf patients will appear in 1,000 newborns, and about 3 / 5 of the deaf patients are due to genetics, and some of them may also be caused by the external environment. Due to physical defects, their daily life is different from that of hearing people. This not only makes them suffer a lot, but also brings a lot of trouble to their families and brings a heavy burden to the society. With the development of social economy and the improvement of people's living standards, hearing and language rehabilitation training can assist deaf patients to restore language function t...

Claims

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Application Information

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IPC IPC(8): A61B5/0484A61B5/00
CPCA61B5/4848A61B5/7235A61B5/7264A61B5/7275A61B5/316A61B5/378
Inventor 郑亿庆梁茂金蔡跃新
Owner SUN YAT SEN MEMORIAL HOSPITAL SUN YAT SEN UNIV
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