School of Instrument Science and Engineering, Southeast University, Nanjing 210000, China.
Science and Technology on Information Systems Engineering Laboratory, The 28th Research Institute of CETC, Nanjing 210000, China.
J Healthc Eng. 2022 Apr 29;2022:8997108. doi: 10.1155/2022/8997108. eCollection 2022.
During sleep, the two hemispheres display asymmetries in their activation pattern. Various hemispheric asymmetry measures have been utilized in existing works. Nevertheless, all these measures have one common problem that they would merely take one representative quantity into account when evaluating the functional asymmetry. However, there is a complex series of information exchanges between the two cerebral hemispheres, and only considering one quantity inevitably leads to one-sided or even incorrect conclusions. Consequently, to address the limitation of conventional laterality indices, we propose the so-called enhanced laterality index (ELI), which considers multiple measures of functional asymmetries. Normal sleep and obstructive sleep apnea electroencephalograms (EEGs) from 21 subjects collected in the clinical acquisition system are applied, and two representative quantities are considered simultaneously in this paper. We measure the signal complexity by using fuzzy entropy, and the signal strength is evaluated by calculating EEG energy. The difference of ELI is demonstrated by the comparison with the traditional laterality index (LI) in evaluating the functional asymmetry during sleep.
在睡眠过程中,两个半球的激活模式表现出不对称性。现有的研究工作中使用了各种半球不对称性度量方法。然而,所有这些方法都存在一个共同的问题,即在评估功能不对称性时,它们仅仅考虑一个代表性的数量。然而,两个大脑半球之间存在着一系列复杂的信息交换,仅仅考虑一个数量不可避免地会导致片面甚至错误的结论。因此,为了解决传统侧性指数的局限性,我们提出了所谓的增强侧性指数(ELI),它考虑了多个功能不对称性的度量。本文应用了 21 名受试者在临床采集系统中采集的正常睡眠和阻塞性睡眠呼吸暂停脑电图(EEG),同时考虑了两个代表性的数量。我们使用模糊熵来测量信号的复杂度,并用 EEG 能量来评估信号的强度。通过与传统的侧性指数(LI)在评估睡眠期间功能不对称性的比较,证明了 ELI 的差异。