CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China.
Comput Intell Neurosci. 2022 Jun 17;2022:6486570. doi: 10.1155/2022/6486570. eCollection 2022.
Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.
癫痫发作是最常见的慢性神经系统疾病之一，它会瞬间打乱患者的生活方式。为了开发用于癫痫管理的新型有效技术，最近的诊断方法侧重于开发基于机器/深度学习模型（ML/DL）的脑电图（EEG）方法。重要的是，脑电图的非侵入性和提供与癫痫相关的电生理信息的重复模式的能力，促使近年来开发了各种用于癫痫发作诊断的 ML/DL 算法。然而，脑电图的低幅度和非平稳特性使得现有的 ML/DL 模型难以实现一致和令人满意的诊断结果，尤其是在临床环境中，几乎无法避免环境因素。尽管最近有几项研究探讨了使用基于脑电图的 ML/DL 方法和统计特征进行癫痫诊断，但不清楚这些工作的优势和局限性是什么，这可能会阻碍癫痫诊断领域的研究和开发进展，也无法为选择基于脑电图的癫痫诊断的 ML/DL 模型和统计特征提取方法提供适当的标准。因此，本文试图通过对基于脑电图的 ML/DL 技术在癫痫诊断中的最新发展进行广泛的系统综述来弥合这一研究差距。在综述中，仔细审查和比较了当前癫痫诊断的发展、各种统计特征提取方法、ML/DL 模型及其在基于脑电图的癫痫诊断中的性能、局限性和核心挑战。此外，还讨论了选择基于脑电图的癫痫诊断的合适和高效特征提取技术和 ML/DL 模型的适当标准。本研究的结果将帮助研究人员决定最有效的 ML/DL 模型和最优的特征提取方法，以提高基于脑电图的癫痫检测性能。
