Brain Network Connectivity Analysis of Different ADHD Groups Based on CNN-LSTM Classification Model

He，Yuchao1,3; Wang，Cheng1,2; Wang，Xin1,2; Zhu，Mingxing4; Chen，Shixiong1; Li，Guanglin1

10.1007/978-3-031-13822-5_56

2022

15th International Conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

0302-9743

1611-3349

978-3-031-13821-8

LECT NOTES ARTIF INT

13456 LNAI

626-635

AUG 01-03, 2022

null,Harbin,PEOPLES R CHINA

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

Attention deficit hyperactivity disorder (ADHD), as a common disease of adolescents, is characterized by the inability to concentrate and moderate impulsive behavior. Since the clinical level mostly depends on the doctor's psychological and environmental analysis of the patient, there is no objective classification standard. ADHD is closely related to the signal connection in the brain and the study of its brain connection mode is of great significance. In this study, the CNN-LSTM network model was applied to process open-source EEG data to achieve high-precision classification. The model was also used to visualize the features that contributed the most, and generate high-precision feature gradient data. The results showed that the traditional processing of original data was different from that of gradient data and the latter was more reliable. The strongest connections in both ADHD and ADD patients were short-range, whereas the healthy group had long-range connections between the occipital lobe and left anterior temporal regions. This study preliminarily achieved the research purpose of finding differences among three groups of people through the features of brain network connectivity.

ADHD Brain network connectivity CNN-LSTM model

Others

English

EI ; CPCI-S

National Natural Science Foundation of China["81927804","62101538"] ; Shenzhen Governmental Basic Research Grant[JCYJ20180507182241622] ; Science and Technology Planning Project of Shenzhen["JSGG20210713091808027","JSGG20211029095801002"] ; China Postdoctoral Science Foundation[2022M710968] ; SIAT Innovation Program for Excellent Young Researchers[E1G027] ; CAS President's International Fellowship Initiative Project[2022VEA0012]

Computer Science ; Robotics

Computer Science, Artificial Intelligence ; Robotics

WOS:000870561700055

20223412602526

Data handling ; Long short-term memory

Biomedical Engineering:461.1 ; Data Processing and Image Processing:723.2

2-s2.0-85136116525

Scopus

1.CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems,Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,Guangdong,518055,China
2.Shenzhen College of Advanced Technology,University of Chinese Academy of Sciences,Shenzhen,Guangdong,518055,China
3.College of Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
4.School of Electronics and Information Engineering,Harbin Institute of Technology,Shenzhen,518055,China

He，Yuchao,Wang，Cheng,Wang，Xin,et al. Brain Network Connectivity Analysis of Different ADHD Groups Based on CNN-LSTM Classification Model[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022:626-635.