Epilepsy detection method based on adaptive weighted feature fusion of deep network stack model

A model adaptive and adaptive weighting technology, applied in the field of intelligent medical signal and image processing, can solve problems such as inability to accurately predict epilepsy time, and achieve the effect of improving epilepsy prediction effect, speeding up training, and reducing the risk of overfitting.

Active Publication Date: 2019-09-27
HANGZHOU DIANZI UNIV
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Problems solved by technology

Pre-seizure timing cannot be accurately predicted due to the inherent limitations of this prediction method

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  • Epilepsy detection method based on adaptive weighted feature fusion of deep network stack model
  • Epilepsy detection method based on adaptive weighted feature fusion of deep network stack model
  • Epilepsy detection method based on adaptive weighted feature fusion of deep network stack model

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Embodiment Construction

[0041] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0042] The first main step of the present invention is the extraction of the average amplitude spectrum feature and the division of the data set, and its specific implementation steps are as follows:

[0043] 1-1. Organize the information of each channel of the EEG signal, generally adjust the EEG signal to 13 channels, and divide it into several samples with a duration of 2 seconds (there is a 1-second overlap between each two adjacent samples) part).

[0044] 1-2. Divide the hour before the seizure on average into three periods, namely the first period, the second period and the third period, and record it as the seizure period when the seizure occurs, and record the time between seizures before and after the four hours as the interval between seizures , and use this to set labels for the samples described in 1-1.

[0045] 1-3. For each sa...

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Abstract

The invention discloses an epilepsy detection method based on adaptive weighted feature fusion of a deep network stack model. The method comprises the following steps of: 1, after an original electroencephalogram signal is preprocessed, conducting discrete Fourier transform on data of each channel of the electroencephalogram signal, and obtaining an average amplitude spectrum of the electroencephalogram signal; randomly dividing the obtained average amplitude spectrum features into several parts so as to be suitable for a deep network stack model of adaptive weighted feature fusion; 2, performing second feature extraction on the data set obtained in the step 1 by using convolutional neural networks of different structures; and 3, performing adaptive weighted feature fusion on the features extracted by different convolutional neural networks, and finally predicting the category of the sample by an error correction output coding model based on a support vector machine. According to the invention, through a stack integration method and an adaptive weighted feature fusion algorithm, the system can fuse features extracted by depth networks of different structures, and the epilepsy prediction effect is improved.

Description

technical field [0001] The invention belongs to the field of intelligent medical signal and image processing, and relates to an epilepsy detection method based on adaptive weighted feature fusion of a deep network stack model. Background technique [0002] With the development of machine learning, related methods and ideas of machine learning are also applied in the field of intelligent medical signal and image processing. The model structure and classification effect of existing epilepsy prediction algorithms still need to be improved, mainly in the following two aspects: [0003] 1. Traditional epilepsy prediction usually roughly divides the patient's brain computer signals into three stages: interictal, preictal, and ictal, and predicts the time of epileptic seizures according to the stage the patient is in. Due to the inherent limitations of this prediction method, it is not possible to accurately predict the time of pre-seizure. [0004] 2. The traditional convolution...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H30/20G06K9/62G06N3/04
CPCG16H30/20G06N3/045G06F18/2411
Inventor 曹九稳祝家华
Owner HANGZHOU DIANZI UNIV
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