DBN based ADHD discriminatory analysis method

Abstract

The utility model relates to a DBN (Deep Belief Network) based ADHD (Attention Deficit Hyperactivity Disorder) discriminatory analysis method. The ADHD discriminatory analysis method comprises the following steps: step 1, pre-processing; step 2, characteristic extracting and classifying: depending on the DBN that is formed by stacking RBMs (Restricted Boltzmann Machines), classified and reversely adjusted in a layer-by-layer manner through softmax finally. The targets of the RBMs in layer-by-layer training are to maximize the likelihood function of the probability function, to introduce in the comparison divergence, and to update the weight function, so that the hidden layer becomes the approximate representation of the visible layer, the hidden layer of the first layer serves as the visible layer of the second layer, by parity of reasoning, the RBM layers of the DBN are obtained, and the last hidden layer is adopted as the input of the softmax to obtain the corresponding output, namely, the classification. The adopted DBN is a probability generative model, is formed by stacking the multiple RBMs with the hidden layers and the visible layers, simulates the layer-by-layer abstract characteristic process when the human brain processes signals, and abstracts the equivalent characteristic expression of the original signals to apply in the field of ADHD classification.

Description

technical field [0001] The invention relates to an ADHD discriminant analysis based on a deep belief network in the field of brain imaging intelligent computing. technical background [0002] ●Attention deficit hyperactivity disorder [0003] Attention deficit hyperactivity disorder (ADHD) is one of the most common diseases in children. It can continue into adolescence and even adulthood. It mainly manifests as inattention, hyperactivity and impulsiveness. The American Psychiatric Association's Diagnostic and Statistical Manual, Fifth Edition is commonly used to help mental health professionals diagnose ADHD. However, diagnosis based on clinical and rating alone may be unreliable, as it can be clinician-specific, cultural and national, etc. Therefore, an objective ADHD diagnostic method will be of great significance. [0004] Functional magnetic resonance imaging (fMRI) is a functional brain imaging technique that establishes brain activity in healthy and diseased individ...

AI Technical Summary

Problems solved by technology

However, DBNs have never been applied in the field of ADHD classification

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