Asgarinejad Fatemeh, Thomas Anthony, Rosing Tajana
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:536-540. doi: 10.1109/EMBC44109.2020.9175328.
Recent years have seen a growing interest in the development of non-invasive devices capable of detecting seizures which can be worn in everyday life. Such devices must be lightweight and unobtrusive which severely limit their on-board computing power and battery life. In this paper, we propose a novel technique based on hyperdimensional (HD) computing to detect epileptic seizures from 2-channel surface EEG recordings. The proposed technique eliminates the need for complicated feature extraction techniques required in conventional ML algorithms. The HD algorithm is also simple to implement and does not require expert knowledge for architectural optimizations needed for approaches based on neural networks. In addition, our proposed technique is light-weight and meets the computation and memory constraints of ultra-small devices. Experimental results on a publicly available dataset indicates our approach improves the accuracy compared to state-of-the-art techniques while consuming smaller or comparable power.
近年来，人们对开发能够检测癫痫发作的无创设备越来越感兴趣，这种设备可以在日常生活中佩戴。此类设备必须轻巧且不引人注目，这严重限制了其板载计算能力和电池寿命。在本文中，我们提出了一种基于超维（HD）计算的新技术，用于从两通道表面脑电图记录中检测癫痫发作。所提出的技术无需传统机器学习算法所需的复杂特征提取技术。HD算法也易于实现，并且不需要基于神经网络的方法进行架构优化所需的专业知识。此外，我们提出的技术重量轻，满足超小型设备的计算和内存限制。在一个公开可用数据集上的实验结果表明，与现有技术相比，我们的方法提高了准确率，同时消耗的功率更小或相当。
