利用机器学习和深度学习检测ADHD的脑电图数据分类技术综述

Q4 Medicine Revista Romana de Pediatrie Pub Date : 2023-06-30 DOI:10.37897/rjp.2023.2.1
Nitin Ahire, R. Awale, Abhay Wagh
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引用次数: 0

摘要

患有注意力缺陷/多动障碍(ADHD)的儿童患有慢性行为疾病。患有多动症的儿童很难集中注意力和控制自己的行为。控制和治疗这种疾病最困难的问题之一是早期发现。目前还没有发现一种可靠的专业程序来早期发现这种情况。脑电图(EEG)是研究多动症的一种有用的神经成像技术;其中一个关键目标是定义ADHD青少年的脑电图。文献中已经提出了许多基于EEG信号的方法来解决这个问题,因为它们是研究ADHD的有效神经成像方法。然而,诊断多动症的最佳记录格式和渠道并不是许多研究的主题。本文讨论了使用基于脑电图的工具识别ADHD的机器学习(ML)和人工智能(AI)策略。尽管在多动症的情况下,ML和AI方法的使用受到限制。然而,这些数据清楚地表明,将脑电图技术与ML/AI相结合可以用于检测多动症。为了根据脑电图功率谱对成人ADHD亚型进行分类,提出了包含多个分类器的ML算法。一种广泛使用的深度学习(DL)方法是卷积神经网络(CNN)。另一方面,DL方法在ADHD研究中的使用目前受到限制。脑电图已被用于研究多动症的神经联系。深度学习算法的最新进展,特别是CNN,有望克服这一问题。
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Comprehensive review of EEG data classification techniques for ADHD detection using machine learning and deep learning
Children who have Attention-Deficit/Hyperactivity Disorder (ADHD) have a chronic behavioral disease. Children with ADHD have a hard time focusing and controlling their actions. One of the most difficult problems in controlling and treating this condition is early detection. There is yet to be discovered a reliable professional procedure for early detection of this condition. The electroencephalogram (EEG) is a useful neuroimaging technique for researching ADHD; one of the key goals is to define the EEG of ADHD youngsters. Numerous methods based on EEG signals have been put out in the literature to address this issue since they are an effective neuroimaging approach for studying ADHD. The best recording formats and channels for diagnosing ADHD, however, have not been the subject of many research. Machine learning (ML) and Artificial Intelligence (AI) strategies for identifying ADHD using EEG-based tools are discussed in this paper. Although, in the case of ADHD, the utilization of ML and AI approaches is restricted. However, the data clearly imply that combining EEG technologies with ML/AI may be utilized to detect ADHD. For categorizing adult ADHD subtypes based on EEG power spectra, ML algorithms that incorporate several classifiers are presented. A widely used deep learning (DL) method is the convolutional neural network (CNN). The use of DL approaches in ADHD research, on the other hand, is currently restricted. EEG has been used in studies to look for ADHD neurological connections. Recent advances in deep learning algorithms, particularly CNN, are anticipated to overcome the issue.
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0.10
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0.00%
发文量
15
审稿时长
4 weeks
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