Detection of Attention Deficit Hyperactivity Disorder in children using CEEMDAN-based cross frequency symbolic convergent cross mapping

Danlei Gu; Aijing Lin; Guancen Lin

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Detection of Attention Deficit Hyperactivity Disorder in children using CEEMDAN-based cross frequency symbolic convergent cross mapping

机译：Detection of Attention Deficit Hyperactivity Disorder in children using CEEMDAN-based cross frequency symbolic convergent cross mapping

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The cross-frequency coupling relationship of EEG signals is of great significance to identify abnormal EEG signals and diagnose diseases. This paper proposes a new algorithm, CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise)-based cross-frequency symbolic convergent cross mapping (CEEMDAN CF-SCCM). In the numerical simulation test, we have confirmed that CEEMDAN CF-SCCM is an effective method to quantify the cross-frequency information transmission of complex system signals from the three dimensions of robustness to noise, coupling sensitivity, and data length sensitivity. It can successfully distinguish the driving factors and response factors in the phase-amplitude interaction and has good robustness to noise. With this approach, we examined differences in cross-frequency phase-amplitude coupling between ADHD patients and normal individuals over classical brain frequency bands (Alpha, Beta, Delta, Gamma, Theta). According to the position of the electrodes, the brain was divided into four regions: front, back, left, and right, and the phase-amplitude coupling between different frequencies in each region was compared. Compared with the normal group, there was more information transmission in the anterior region of Delta waves and Theta waves. The front and left sides of the brain are responsible for thinking, mental and auditory functions. This information helps us gain insight into the brain dynamics of ADHD patients and contributes to the diagnosis of the disease.

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来源
《Expert Systems with Application》 |2023年第9期|120105.1-120105.16|共16页
作者
Danlei Gu; Aijing Lin; Guancen Lin;
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作者单位

School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, PR China;

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正文语种英语
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Cross-frequency coupling; Convergent cross mapping; Complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN); Attention Deficit Hyperactivity Disorder(ADHD); Electroencephalogram (EEG); Dispersion;

Detection of Attention Deficit Hyperactivity Disorder in children using CEEMDAN-based cross frequency symbolic convergent cross mapping

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