Deshmukh Manjusha Pradeep, Khemchandani Mahi, Thakur Paramjit Mahesh
Professor, Computer Engineering department, Saraswati College of Engineering, Navi Mumbai, India.
Associate Professor, Information Technology, Saraswati College of Engineering, Navi Mumbai, India.
Appl Neuropsychol Child. 2024 Aug 5:1-13. doi: 10.1080/21622965.2024.2378464.
Attention deficit hyperactivity disorder (ADHD), is a general neurodevelopmental syndrome. This affects both adults and children, causing issues like hyperactivity, inattention, and impulsivity. Diagnosis, typically reliant on patient narratives and questionnaires, can sometimes be inaccurate, leading to distress. We propose utilizing empirical mode decomposition (EMD) for feature extraction and a machine learning (ML) algorithm to categorize ADHD and control.
Publicly available Kaggle dataset is used for research. The EMD technique decomposes an electroencephalogram (EEG) waveform to 12 intrinsic mode functions (IMFs). Thirty-one statistical parameters are generated over the first 6 IMFs to create an input feature vector for the deep belief network (DBN) classifier. Principal component analysis (PCA) is utilized to reduce dimension.
Experimental results are compared on prefrontal cortex channels Fp1 and Fp2. After an in-depth evaluation of all metrics, it is observed that, in patients with ADHD, the prefrontal cortex regulates attention, behavior, and emotion. Our findings align with established neuroscience. The critical functions of the brain, such as organization, planning, attention, and decision making, are performed by the frontal lobe.
Our work provides a novel approach to understanding the disorder's underlying neurobiological mechanisms. It has the potential to deepen our understanding of the condition, improve diagnostic accuracy, personalize treatment methods, and, ultimately, improve outcomes for those affected.
注意力缺陷多动障碍（ADHD）是一种常见的神经发育综合征。它对成人和儿童都会产生影响，导致多动、注意力不集中和冲动等问题。诊断通常依赖于患者的叙述和问卷，有时可能不准确，从而造成困扰。我们建议利用经验模态分解（EMD）进行特征提取，并使用机器学习（ML）算法对ADHD和对照组进行分类。
使用公开可用的Kaggle数据集进行研究。EMD技术将脑电图（EEG）波形分解为12个本征模函数（IMF）。在前6个IMF上生成31个统计参数，以创建深度信念网络（DBN）分类器的输入特征向量。利用主成分分析（PCA）进行降维。
在额叶前部皮层通道Fp1和Fp2上对实验结果进行了比较。在对所有指标进行深入评估后发现，ADHD患者的前额叶皮层调节注意力、行为和情绪。我们的发现与已有的神经科学研究结果一致。大脑的关键功能，如组织、计划、注意力和决策，由额叶执行。
我们的工作提供了一种理解该疾病潜在神经生物学机制的新方法。它有可能加深我们对这种疾病的理解，提高诊断准确性，使治疗方法个性化，并最终改善患者的治疗效果。
