论文标题
在癫痫发作检测的用例上，用于特征选择的高维计算
Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection
论文作者
论文摘要
医疗保健局势正在从重点是症状治疗的反应性干预措施转变为更积极的预防措施，从一小撮适合的药物到个性化医学，从集中式范式到分布式范式。可穿戴的物联网设备和用于连续监测的新型算法是该过渡的重要组成部分。高维（HD）计算是受神经科学研究启发的新兴ML范式，对物联网设备和生物医学应用有趣的各个方面。在这里，我们探讨了尚未解决的时空数据最佳编码的主题，例如脑电图（EEG）信号及其所带来的所有信息。此外，我们演示了如何使用足够的编码来使用高清计算框架来执行特征选择。据我们所知，这是使用文献中使用高清计算进行功能选择的第一种方法。结果，我们认为它可以支持ML社区，以与功能和渠道选择有关的多个方向以及模型的解释性进一步促进研究。
The healthcare landscape is moving from the reactive interventions focused on symptoms treatment to a more proactive prevention, from one-size-fits-all to personalized medicine, and from centralized to distributed paradigms. Wearable IoT devices and novel algorithms for continuous monitoring are essential components of this transition. Hyperdimensional (HD) computing is an emerging ML paradigm inspired by neuroscience research with various aspects interesting for IoT devices and biomedical applications. Here we explore the not yet addressed topic of optimal encoding of spatio-temporal data, such as electroencephalogram (EEG) signals, and all information it entails to the HD vectors. Further, we demonstrate how the HD computing framework can be used to perform feature selection by choosing an adequate encoding. To the best of our knowledge, this is the first approach to performing feature selection using HD computing in the literature. As a result, we believe it can support the ML community to further foster the research in multiple directions related to feature and channel selection, as well as model interpretability.
