Dept. of Electrical and Electronic Engineering, University College Cork, Ireland.
Comput Methods Programs Biomed. 2012 Dec;108(3):1206-15. doi: 10.1016/j.cmpb.2012.06.005. Epub 2012 Aug 9.
The objective of this study is to develop methods to dynamically select EEG channels to reduce power consumption in seizure detection while maintaining detection accuracy. A method is proposed whereby a number of primary screening channels are predefined. Depending on the classification results of those channels, further channels are selected for analysis. This method provides savings in computational complexity of 43%. A further method called idling is then proposed which increases the computational saving to 75%. The performance of a location-independent, decision-based method is used for comparison. The proposed method achieves better computational savings for the same performance than the decision-based method. The decision-based method was capable of higher overall computational savings, but with a reduction in seizure detection performance. Each method was also implemented with the REACT algorithm on a Blackfin microprocessor and the average power measured. The proposed methods gave a power saving of up to 47% with no reduction in detection performance.
本研究旨在开发方法以动态选择 EEG 通道，在保持检测准确性的同时降低癫痫检测的功耗。提出了一种方法，其中预定义了一些主要的筛选通道。根据这些通道的分类结果，选择进一步的通道进行分析。该方法可将计算复杂度降低 43%。然后提出了一种称为空闲的方法，可将计算节省提高到 75%。使用位置独立、基于决策的方法进行比较。与基于决策的方法相比，所提出的方法在相同性能下实现了更好的计算节省。基于决策的方法能够实现更高的整体计算节省，但癫痫检测性能会降低。还在 Blackfin 微处理器上使用基于反应的（REACT）算法实现了每种方法，并测量了平均功率。所提出的方法在不降低检测性能的情况下，可将功耗降低高达 47%。
