Center for Intelligent Medical Electronics (CIME), School of Information Science and Engineering, Fudan University, Shanghai 200433, China.
Department of Industrial Design, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands.
Sensors (Basel). 2018 May 26;18(6):1720. doi: 10.3390/s18061720.
Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications.
复杂性科学为理解各种复杂的自然或社会现象提供了新的视角和机会,包括癫痫等脑功能障碍。通过深入研究电生理信号和神经影像学的复杂性,出现了新的见解。这些发现表明,复杂性是生理过程的一个基本方面。生理过程固有的非线性和非平稳性限制了基于更简单基本假设的方法,指出了更全面理解其行为及其与某些疾病关系的途径。复杂性的观点可以通过提供专门用于理解和干预癫痫的新数据分析工具,从研究和临床实践中受益。这篇综述旨在概述不同学科在癫痫监测领域中研究生物信号复杂性的方法。尽管生物信号的复杂性仍未被完全理解,但已经获得了大量新的见解。尽管通过离线分析在癫痫发作检测和预测方面取得了有希望的结果,但我们仍然缺乏稳健、经过验证的实时应用。需要多学科合作和更多高质量的数据供整个社区使用,以进行可重复的研究和开发此类应用。