Eeg high-frequency oscillation signal detecting system based on convolution neural network

Abstract

The invention belongs to the field of medical signal processing, particularly provides an eeg high-frequency oscillation signal detecting system based on a convolution neural network, and aims to solve the problem that in a conventional eeg high-frequency oscillation signal detection technique, the false drop rate is high caused by high-frequency noise and peaked wave shape. The eeg high-frequency oscillation signal detecting system comprises a user terminal, a data preprocessing module, a high-frequency oscillation signal predetecting module, the convolution neural network module and a static module, wherein the user terminal is used for acquiring an eeg signal; the data preprocessing module is used for performing data preprocessing; the high-frequency oscillation signal predetecting module is used for performing detection on the eeg signal to obtain suspected high-frequency oscillation fragments; and the convolution neural network module is used for classifying all the suspected high-frequency oscillation fragments. According to the eeg high-frequency oscillation signal detecting system disclosed by the invention, when HFOs are detected, the sensitivity can be effectively improved, the false drop rate is reduced, and the precision rate of epileptogenic focus positioning is increased; and besides, the eeg high-frequency oscillation signal detecting system can use scalp eeg and can also use cortex eeg, so that the egg high-frequency oscillation signal detecting system can enable a doctor to achieve more flexibility.

Description

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AI Technical Summary

Benefits of technology

This technology allows users with varying preferences to selectively filter data based on their requirements without sacrificing accuracy. Additionally it offers more flexible options that allow us to adjust how much sensitive we want our equipment while reducing its overall size. Overall this makes processing faster and easier by providing accurate results even at lower cost per unit area.

Problems solved by technology

Technical Problem: Current techniques used for automatically identifying and locating brain foci require long periods of training before they become symptomsatic. Existing automated systems lack accuracy due to factors like human judgment errors and limitations associated with manually identified areas. Therefore, it would be beneficial to develop better ways to identify and track these regions while reducing their impact on patient outcomes during treatment procedures.

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