Children epilepsy syndrome classification method based on transfer learning multi-model decision fusion

Abstract

The invention discloses a children epilepsy syndrome classification method based on transfer learning multi-model decision fusion. The method comprises the following steps: step 1, performing digitalfiltering on original multi-channel EEG signal data to delete and select a frequency band, and then performing artifact elimination based on a Riemannian geometry unsupervised clustering algorithm andabnormal data point elimination based on median filtering to obtain a pure EEG signal; step 2, extracting MFCC features, LPCC features, wavelet packet features and statistical features from the EEG signals; and step 3, inputting the MFCC and LPCC feature pictures and training a model F1, inputting wavelet packet features and statistical features and training a model F2, performing weighted summation on SoftMax probability output layers of the model F1 and the model F2, and obtaining a child epileptic syndrome category to which the sample belongs according to the obtained final probability. The method can achieve the precise classification of the children epileptic syndrome.

Description

technical field [0001] The invention belongs to the field of machine learning and intelligent biomedical signal processing, and relates to a classification method for children's epilepsy syndrome based on transfer learning multi-model decision fusion. Background technique [0002] Epilepsy in children is a common disease of the chronic nervous system, and its incidence rate is 10-15 times that of adult epilepsy, which seriously threatens the life and health of children. The World Health Organization pointed out in 2018 that there are about 7‰-8‰ of epilepsy patients in the world, and 75%-80% of the onset age of epilepsy is before the age of 18, and the incidence rate is generally the highest within 4 years old. Although modern epilepsy treatment technology is becoming more and more advanced, and the cure rate of epilepsy is gradually increasing, the morbidity and mortality rate of childhood epilepsy have not decreased significantly. The main reasons include: limited medical ...

AI Technical Summary

Problems solved by technology

Although modern epilepsy treatment technology is becoming more and more advanced, and the cure rate of epilepsy is gradually increasing, the morbidity and mortality rate of childhood epilepsy have not decreased significantly. The main reasons include: limited medical resources, lack of efficient early detection of epilepsy and intelligent auxiliary diagnosis Systematic, non-targeted intervention treatment and follow-up evaluation system

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