EEG (electroencephalogram) signal unsupervised feature learning method based on convolutional network and self encoding

Abstract

The invention relates to an EEG (electroencephalogram) signal unsupervised feature learning method based on a convolutional network and self encoding and belongs to the convolutional network field. The method includes the following steps that: predictive processing is performed on the data of EEG data samples, the data are mapped into an interval of [0,1] through 0-1 standardization, namely, linear transformation; an AE-CDNN model is constructed on the basis of a deep convolutional network and self-coding, training data are used to train the AE-CDNN model; and the dimensionality of the trainedAE-CDNN model is decreased to a low dimensionality through using test data, and therefore, the classification of the EEG data can be benefitted. The AE-CDNN model is constructed on the basis of the deep convolutional network and self-coding and is adopted to perform unsupervised feature learning of epilepsy EEG signals; feature learning and classification are performed on two public epilepsy EEGdata sets; and the AE-CDNN model can be well applied to the extraction of epilepsy brain signal features.

Description

technical field [0001] The invention relates to a convolutional network, in particular to an unsupervised feature learning method for EEG signals based on a convolutional network and self-encoding. Background technique [0002] Epilepsy is a non-infectious chronic brain disease that affects people of all ages. There are currently about 50 million epilepsy patients worldwide. It has become one of the most common neurological diseases worldwide [1] . Epilepsy causes cognitive dysfunction (such as loss of consciousness, consciousness, etc.) to the patient, which will bring great physical harm to the patient (such as fracture and injury during the seizure), and it will also bring great psychological harm to the patient due to humiliation and discrimination. pain of. Because epileptic seizures can cause irreversible damage to the brain of patients and have the characteristics of unprovoked repeated seizures, how to accurately analyze and prevent epilepsy is of great significanc...

AI Technical Summary

Problems solved by technology

[0005] Although experienced experts can identify epilepsy EEG and scholars have done a lot of research on the problem of epilepsy detection based on EEG, there are still many challenges in the automatic detection of epilepsy.

Difficulties in feature design and selection, and applicability of these approaches to new patients remains unknown

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