Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization
Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization
复制标题
DOI：
10.3390/app14010273
复制
发表时间：
2023-12
期刊：
影响因子：
--
通讯作者：
Soo-Yeon Ji;S. Jayarathna;A. M. Perrotti;Katrina Kardiasmenos;Dong H. Jeong
中科院分区：
文献类型：
--
作者：
Soo-Yeon Ji;S. Jayarathna;A. M. Perrotti;Katrina Kardiasmenos;Dong H. Jeong
神经障碍会导致各种健康和心理挑战，影响生活质量，并给诊断出患有这些疾病的个人及其护理人员带来经济负担。由人类神经系统故障引起的异常大脑活动是神经系统疾病的特征。因此，早期识别这些异常对于设计旨在促进和维持生活质量的适当治疗和干预措施至关重要。脑电图（EEG）是一种监测大脑活动的非侵入性方法，经常用于检测神经和精神疾病中的异常大脑活动。这项研究引入了一种方法，通过基于从神经和精神障碍患者收集的脑电图信号整合特征提取、机器学习和视觉分析，扩展对神经障碍的理解和识别。使用机器学习技术评估四种特征方法（脑电图频带、原始数据、功率谱密度和小波变换）的分类性能，以评估它们在短脑电图分段（一秒和两秒）中区分神经功能障碍的能力。具体来说，分类分析是在两种情况下进行的：基于单通道的分类和基于区域的分类。虽然正常（健康）和异常（神经障碍）脑电图指标之间的明确界限可能并不明显，但可以通过使用小波特征的可视化来观察它们的相似性和区别。值得注意的是，额叶大脑区域（额叶）成为区分不同大脑区域异常的关键区域。此外，小波特征和视觉分析的结合在识别和理解神经障碍方面被证明是有效的。
Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities by integrating feature extraction, machine learning, and visual analysis based on EEG signals collected from individuals with neurological and mental disorders. The classification performance of four feature approaches—EEG frequency band, raw data, power spectral density, and wavelet transform—is assessed using machine learning techniques to evaluate their capability to differentiate neurological disabilities in short EEG segmentations (one second and two seconds). In detail, the classification analysis is conducted under two conditions: single-channel-based classification and region-based classification. While a clear demarcation between normal (healthy) and abnormal (neurological disabilities) EEG metrics may not be evident, their similarities and distinctions are observed through visualization, employing wavelet features. Notably, the frontal brain region (frontal lobe) emerges as a crucial area for distinguishing abnormalities among different brain regions. Also, the integration of wavelet features and visual analysis proves effective in identifying and understanding neurological disabilities.