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Auxiliary diagnosis method and system based on deep learning of electroencephalograms

A deep learning and auxiliary diagnosis technology, applied in the field of disease diagnosis, can solve the problems of low diagnosis efficiency and low accuracy of automatic diagnosis of epilepsy, and achieve the effect of improving diagnosis efficiency and ensuring accuracy

Active Publication Date: 2018-12-18
CENT SOUTH UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide an auxiliary diagnosis method and system based on EEG deep learning to solve the problems of low accuracy and low diagnostic efficiency of automatic diagnosis of epilepsy

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  • Auxiliary diagnosis method and system based on deep learning of electroencephalograms
  • Auxiliary diagnosis method and system based on deep learning of electroencephalograms
  • Auxiliary diagnosis method and system based on deep learning of electroencephalograms

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Embodiment 1

[0052] refer to figure 1 , the present embodiment provides a method for auxiliary diagnosis based on EEG deep learning, including:

[0053] S10: Obtain the collected EEG sample data, integrate the EEG sample data into a preset normalized model, and obtain normalized EEG integer data;

[0054] S20: Convert the normalized EEG integer data into a word embedding vector according to a preset word embedding model;

[0055]S30: Perform feature extraction on the word embedding vector according to a preset deep learning model, and perform time stamping and identification and diagnosis on the extracted features;

[0056] S40: According to the time stamp and identification diagnosis, output the probability of disease onset, and distinguish the EEG sample data whose disease onset probability exceeds a preset probability.

[0057] In this application, the corresponding medical equipment is used to diagnose the diseased person, that is, the corresponding diagnostic steps and systems are s...

Embodiment 2

[0092] refer to figure 2 , the present embodiment provides an auxiliary diagnosis system based on EEG deep learning, including:

[0093] Input normalization module 21: used to obtain the collected EEG sample data, integrate the EEG sample data into a preset normalization model, and obtain normalized EEG integer data;

[0094] Word embedding module 22: for converting the normalized EEG integer data into a word embedding vector according to a preset word embedding model;

[0095] Deep learning module 23: used to perform feature extraction on the word embedding vector according to a preset deep learning model, and perform time stamping and identification and diagnosis on the extracted features;

[0096] Output module 24: used for outputting the disease onset probability according to the time stamp and identification diagnosis, and distinguishing the EEG sample data whose disease onset probability exceeds a preset probability.

[0097] In this embodiment, the word embedding mod...

Embodiment 3

[0113] refer to image 3 , this embodiment provides a flow chart of an auxiliary diagnosis method based on deep learning of EEG. The patient is scanned by a scanner to obtain an EEG sample image, and the format and value of the scanner input data are integrated into the standardized In the model, the input matrix format is obtained; through the word embedding model, the EEG database data is converted and compressed into a low-latitude word embedding vector with features; and the word embedding vector is marked and identified and diagnosed through the deep learning model; finally, the The output module gives the seizure flag of 0 or 1, and the probability of the disease, wherein, when it is 0, it means no epilepsy, and 1 means epileptic seizure.

[0114] Taking this example as an example, the training set included 118 patients with seizure EEG and 146 patients with normal background EEG. The total recording time was 330.08 hours. The test set included 38 patients with seizure E...

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Abstract

The invention provides an auxiliary diagnosis method and system based on deep learning of electroencephalograms. The auxiliary diagnosis method and system are used for solving a problem that diagnosisaccuracy of epilepsy diseases is not high. The method comprises the following steps: S10: collected electroencephalogram sample data is obtained, the electroencephalogram sample data is integrated into a preset normalization model, and standardized electroencephalogram integer data is obtained; S10: according to a preset word embedding model, the standardized electroencephalogram integer data isconverted into a word embedding vector; S30: features of the word embedding vector are extracted according to a preset deep learning model, and the extracted features are subjected to time stamping operation and identifying and diagnosing operation; S40: according to the time stamping operation and the identifying and diagnosing operation, disease attack probability is output, and electroencephalogram sample data of which the disease attack probability exceeds preset probability is distinguished. Via the auxiliary diagnosis method and system disclosed in the invention, electroencephalograms ofa patient can be automatically diagnosed via a training model, time zones of epileptic seizures in the electroencephalograms can be automatically identified and marked, probability of contracting diseases can be obtained, work efficiency of clinicians can be improved, and diagnosis efficiency can be increased.

Description

technical field [0001] The present invention relates to the technical field of disease diagnosis, in particular to an auxiliary diagnosis method and system based on electroencephalogram deep learning. Background technique [0002] Epilepsy is a transient brain dysfunction caused by sudden abnormal excessive discharge of brain neurons. The annual incidence is high. The detection and identification of EEG signals is the most important means of diagnosing epilepsy. The electroencephalogram scanner collects the weak bioelectricity generated by the human brain itself at the scalp, and enlarges the recorded curve. It is the main means and basis for clinicians to diagnose various brain-related diseases, especially epilepsy. or seizures. [0003] The recorded electrical signals that do not originate from the brain are called artifacts. Artifacts can be composed of various factors such as eye muscle activity, poor contact of EEG electrodes, swallowing actions, and head displacement....

Claims

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Application Information

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IPC IPC(8): A61B5/0476A61B5/00
CPCA61B5/7235A61B5/7271A61B5/7275A61B5/316A61B5/369
Inventor 陈志刚肖雨桐刘佳琦
Owner CENT SOUTH UNIV
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