Abstract
In the treatment of children with autistic spectrum disorder (ASD) through music perception, the perception effect and the development of the disease are mainly reflected in the fluctuations of the electroencephalogram (EEG), which is clinically effective on the brain. There is an inaccuracy problem in electrogram judgment, and deep learning has great advantages in signal feature extraction and classification. Based on the theoretical basis of Deep Belief Network (DBN) in deep learning, this paper proposes a method that combines the optimized Restricted Boltzmann machine (RBM) feature extraction model with the softmax classification algorithm. Brain wave tracking analysis is performed on children with autism who have received different music perception treatments to improve classification accuracy and achieve the purpose of accurately judging the condition. Through continuous adjustment and optimization of the weight matrix in the model, a stable recognition model is obtained. The simulation results show that this optimization algorithm can effectively improve the recognition performance of DBN, with an accuracy of 94% in a certain environment, and has a better classification effect than other traditional classification methods.
Highlights
Nan ZhaoIn the treatment of children with autistic spectrum disorder (ASD) through music perception, the perception effect and the development of the disease are mainly reflected in the fluctuations of the electroencephalogram (EEG), which is clinically effective on the brain
With the rapid development of music perception diagnosis and treatment, clinical music perception diagnosis and treatment of children with autism are gradually expanding
Aiming at the shortcomings of traditional Deep Belief Network (DBN) recognition performance that is not very high, this paper proposes an improved algorithm for DBN EEG recognition based on the dual features of frequency band energy ratio (FBER) and moving average sample entropy (MVSE)
Summary
In the treatment of children with autistic spectrum disorder (ASD) through music perception, the perception effect and the development of the disease are mainly reflected in the fluctuations of the electroencephalogram (EEG), which is clinically effective on the brain. Based on the theoretical basis of Deep Belief Network (DBN) in deep learning, this paper proposes a method that combines the optimized Restricted Boltzmann machine (RBM) feature extraction model with the softmax classification algorithm. Brain wave tracking analysis is performed on children with autism who have received different music perception treatments to improve classification accuracy and achieve the purpose of accurately judging the condition. E simulation results show that this optimization algorithm can effectively improve the recognition performance of DBN, with an accuracy of 94% in a certain environment, and has a better classification effect than other traditional classification methods
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