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Epilepsy detection method based on generative adversarial network

A detection method and network technology, applied in diagnostic recording/measurement, medical science, sensors, etc., can solve the problems of small sample size, large error in experimental results, small data set size, etc., and achieve the effect of improving accuracy

Inactive Publication Date: 2019-12-17
NORTHWEST UNIV
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Problems solved by technology

In terms of epilepsy detection, there are also many new developments based on convolutional neural networks. However, when doing epilepsy detection experiments, using convolutional neural networks for training requires a large number of data samples, and most public data sets come from a small number of patients. , there are few samples in the data set and the sample size is small, which will have a certain degree of impact on the accuracy of the epilepsy detection results.
[0005] At present, when doing epilepsy detection experiments, we need to use a large number of data sets, generally speaking, we use public data sets, but most of the public data sets come from a small number of patients, the size of the data set is small, and the sample size is small. The CNN network needs a large number of data samples for training, which will cause large errors in the experimental results.

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  • Epilepsy detection method based on generative adversarial network
  • Epilepsy detection method based on generative adversarial network
  • Epilepsy detection method based on generative adversarial network

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Experimental program
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Embodiment

[0072] Step 1: Preprocessing the original EEG signal, specifically including the following steps:

[0073] [1] The data set selected for this experiment is the CHB-MIT data set, which was collected at Boston Children's Hospital, including EEG recordings from pediatric subjects with intractable seizures, from 22 subjects (5 Males, 3-22 years; 17 females, 1.5-19 years) collected records from 23 cases. Each case contains 9 to 42 EEG signal records from a single patient, each signal acquisition time is 1 hour, and a small amount of signal acquisition time is 2 hours, the sampling frequency is 256HZ, and the resolution is 16 bits.

[0074] [2] Signal preprocessing: cut the EEG signals of all patients into 2s segments, and then perform down-sampling operation on these data, the sampling frequency is 128HZ, use 60HZ IIR notch filter and 1HZ high-pass filter for filtering operation (filtering Eliminate power line interference and baseline drift), distinguish the time segment with sei...

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Abstract

The invention discloses an epilepsy detection method based on a generative adversarial network. According to the method, EEG signals during an epilepsy seizure are generated based on the generative adversarial network, experimental data samples are expanded, the expanded data samples are input into a CNN for training, then the real data samples are used for testing, and the aim of improving accuracy of epilepsy detection is achieved. The epilepsy detection method includes the specific steps which can be described as: step one, data are cut, downsampled and filtered, and processed data fragments are divided into a disease seizure class and a normality class; step two, the generative adversarial network is trained; step three, the trained generative adversarial network is used for generatingepilepsy seizure EGG data; and step four, a training set and a testing set are divided, the CNN is constructed for training and testing, and detection results are analyzed.

Description

technical field [0001] The invention relates to EEG signal processing and an epilepsy detection method, in particular to an epilepsy detection method based on a generative confrontation network. Background technique [0002] Brain-computer interface technology is an emerging technology with good development prospects. With the development of brain-computer interface technology, it plays an increasingly important role in many fields such as human-computer interaction and medical health. An important application of brain-computer interface technology is epilepsy detection, focusing on the processing and analysis of EEG signals. Although significant progress has been made in epilepsy detection methods based on EEG signals at home and abroad in recent years, due to the The influence of unbalanced, dynamic, unstable and other factors poses great challenges to the research of epilepsy detection methods. [0003] Currently, epilepsy detection methods are mainly divided into tradi...

Claims

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Application Information

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IPC IPC(8): A61B5/00A61B5/0476
CPCA61B5/4094A61B5/7225A61B5/7264A61B5/7267A61B5/369
Inventor 高岭郑勇郭红波张侃赵悦蓉王海郑杰杨旭东
Owner NORTHWEST UNIV
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