{"title":"通过多分辨率分析和机器学习技术,对从额叶区收集的脑电图(EEG)数据与受注意力缺陷多动障碍(ADHD)影响的其他脑区进行比较分析。","authors":"Manjusha Deshmukh, Mahi Khemchandani, Paramjit Mahesh Thakur","doi":"10.1080/21622965.2024.2405719","DOIUrl":null,"url":null,"abstract":"<p><p>Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by repeated patterns of hyperactivity, impulsivity, and inattention that limit daily functioning and development. Electroencephalography (EEG) anomalies correspond to changes in brain connection and activity. The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DWT) for feature extraction and machine learning (ML) algorithms to categorize ADHD and control subjects. For this study, the authors considered freely accessible ADHD data obtained from the IEEE data site. Studies have demonstrated a range of EEG anomalies in ADHD patients, such as variations in power spectra, coherence patterns, and event-related potentials (ERPs). Some of the studies claimed that the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD. , Based on the research that claimed the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD, the proposed study examines the optimal position of EEG electrode for identifying ADHD and in addition to monitoring accuracy on frontal/ prefrontal and other regions of brain our study also investigates the position groupings that have the highest effect on accurateness in identification of ADHD. The results demonstrate that the dataset classified with AdaBoost provided values for accuracy, precision, specificity, sensitivity, and F1-score as 1.00, 0.70, 0.70, 0.75, and 0.71, respectively, whereas using random forest (RF) it is 0.98, 0.64, 0.60, 0.81, and 0.71, respectively, in detecting ADHD. After detailed analysis, it is observed that the most accurate results included all electrodes. The authors believe the processes can detect various neurodevelopmental problems in children utilizing EEG signals.</p>","PeriodicalId":8047,"journal":{"name":"Applied Neuropsychology: Child","volume":" ","pages":"1-15"},"PeriodicalIF":1.4000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit hyperactivity disorder (ADHD) through multiresolution analysis and machine learning techniques.\",\"authors\":\"Manjusha Deshmukh, Mahi Khemchandani, Paramjit Mahesh Thakur\",\"doi\":\"10.1080/21622965.2024.2405719\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by repeated patterns of hyperactivity, impulsivity, and inattention that limit daily functioning and development. Electroencephalography (EEG) anomalies correspond to changes in brain connection and activity. The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DWT) for feature extraction and machine learning (ML) algorithms to categorize ADHD and control subjects. For this study, the authors considered freely accessible ADHD data obtained from the IEEE data site. Studies have demonstrated a range of EEG anomalies in ADHD patients, such as variations in power spectra, coherence patterns, and event-related potentials (ERPs). Some of the studies claimed that the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD. , Based on the research that claimed the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD, the proposed study examines the optimal position of EEG electrode for identifying ADHD and in addition to monitoring accuracy on frontal/ prefrontal and other regions of brain our study also investigates the position groupings that have the highest effect on accurateness in identification of ADHD. The results demonstrate that the dataset classified with AdaBoost provided values for accuracy, precision, specificity, sensitivity, and F1-score as 1.00, 0.70, 0.70, 0.75, and 0.71, respectively, whereas using random forest (RF) it is 0.98, 0.64, 0.60, 0.81, and 0.71, respectively, in detecting ADHD. After detailed analysis, it is observed that the most accurate results included all electrodes. 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引用次数: 0
摘要
注意缺陷多动障碍(ADHD)是一种神经发育障碍,其特征是反复出现多动、冲动和注意力不集中,从而限制了日常功能和发育。脑电图(EEG)异常与大脑连接和活动的变化相对应。作者建议利用经验模式分解(EMD)和离散小波变换(DWT)进行特征提取,并利用机器学习(ML)算法对多动症和对照组受试者进行分类。在这项研究中,作者考虑了从 IEEE 数据网站免费获取的 ADHD 数据。研究表明,ADHD 患者存在一系列脑电图异常现象,如功率谱、相干模式和事件相关电位(ERPs)的变化。其中一些研究声称,大脑前额叶皮层和额叶区域在错综复杂的网络中相互协作,其中任何一个区域的失调都会加重多动症的症状。除了监测大脑额叶/前额叶和其他区域的准确性外,我们的研究还调查了对多动症识别准确性影响最大的位置分组。结果表明,使用 AdaBoost 分类的数据集在检测多动症方面的准确度、精确度、特异性、灵敏度和 F1 分数分别为 1.00、0.70、0.70、0.75 和 0.71,而使用随机森林(RF)分类的数据集在检测多动症方面的准确度、精确度、特异性、灵敏度和 F1 分数分别为 0.98、0.64、0.60、0.81 和 0.71。经过详细分析发现,最准确的结果包括所有电极。作者认为,该过程可以利用脑电信号检测儿童的各种神经发育问题。
Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit hyperactivity disorder (ADHD) through multiresolution analysis and machine learning techniques.
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by repeated patterns of hyperactivity, impulsivity, and inattention that limit daily functioning and development. Electroencephalography (EEG) anomalies correspond to changes in brain connection and activity. The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DWT) for feature extraction and machine learning (ML) algorithms to categorize ADHD and control subjects. For this study, the authors considered freely accessible ADHD data obtained from the IEEE data site. Studies have demonstrated a range of EEG anomalies in ADHD patients, such as variations in power spectra, coherence patterns, and event-related potentials (ERPs). Some of the studies claimed that the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD. , Based on the research that claimed the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD, the proposed study examines the optimal position of EEG electrode for identifying ADHD and in addition to monitoring accuracy on frontal/ prefrontal and other regions of brain our study also investigates the position groupings that have the highest effect on accurateness in identification of ADHD. The results demonstrate that the dataset classified with AdaBoost provided values for accuracy, precision, specificity, sensitivity, and F1-score as 1.00, 0.70, 0.70, 0.75, and 0.71, respectively, whereas using random forest (RF) it is 0.98, 0.64, 0.60, 0.81, and 0.71, respectively, in detecting ADHD. After detailed analysis, it is observed that the most accurate results included all electrodes. The authors believe the processes can detect various neurodevelopmental problems in children utilizing EEG signals.
期刊介绍:
Applied Neuropsychology: Child publishes clinical neuropsychological articles concerning assessment, brain functioning and neuroimaging, neuropsychological treatment, and rehabilitation in children. Full-length articles and brief communications are included. Case studies of child patients carefully assessing the nature, course, or treatment of clinical neuropsychological dysfunctions in the context of scientific literature, are suitable. Review manuscripts addressing critical issues are encouraged. Preference is given to papers of clinical relevance to others in the field. All submitted manuscripts are subject to initial appraisal by the Editor-in-Chief, and, if found suitable for further considerations are peer reviewed by independent, anonymous expert referees. All peer review is single-blind and submission is online via ScholarOne Manuscripts.