Epilepsy detection integrated circuit based on sparse limit learning machine algorithm

Abstract

The invention discloses an epilepsy detection integrated circuit based on a sparse limit learning machine algorithm. Signals in electroencephalogram data are divided by a window containing 256 points,and the data type is a 16-bit fixed-point number comprising an 8-bit integer part and an 8-bit decimal part; four-dimensional signals are generated from the electroencephalogram data of the known type through a wavelet transform circuit module, then the generated signals are input into a feature extraction circuit module to obtain eight-dimensional feature vectors, and then the eight-dimensionalfeature vectors are input into a classifier circuit module for training, classified and output; brain electric signals of the unknown type sequentially pass through the wavelet transform circuit module and the feature extraction circuit module to obtain eight-dimensional feature vectors, and the eight-dimensional feature vectors are input into a classifier circuit module, classified and output. Lifting type wavelet transform is used for processing the electroencephalogram signals, features with different frequency bands in a signal time domain and in a frequency domain can be obtained, and results are superior to those of a traditional filter and Fourier transform.

Description

technical field [0001] The invention belongs to the technical field of integrated circuits, and in particular relates to an epilepsy detection integrated circuit based on a sparse extreme learning machine algorithm. Background technique [0002] Epilepsy is caused by abnormal discharge of brain neurons, so epileptic seizures can have a great impact on people's EEG signals. Due to its non-invasive and convenient features, EEG examination plays an important role in the diagnosis of epileptic seizures, and it also helps to classify the types of epileptic seizures. All cases of clinically suspected epilepsy are diagnosed by EEG examination. diagnostic. At present, epilepsy detection through EEG still mainly relies on doctors to manually analyze the patient's EEG signals. This method is not only time-consuming, but also has no unified basis for judgment. its accuracy. [0003] Due to the many shortcomings of manual analysis of signals, coupled with the continuous development o...

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Problems solved by technology

[0004] After searching the existing literature, it is found that there is no report on the implementation of integrated circuits using lifting wavelet transform to process EEG signals, extract features, and then use sparse extreme learning machines for training and classification.

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