Kernel Methods and the Maximum Mean Discrepancy for Seizure Detection

被引:0
|
作者
Hamzi, Boumediene [1 ]
AlOtaiby, Turky N. [2 ]
AlShebeili, Saleh [3 ]
AlAnqary, Arwa [4 ]
机构
[1] Alfaisal Univ, Dept Math, Riyadh, Saudi Arabia
[2] KACST, Riyadh, Saudi Arabia
[3] King Saud Univ, Dept Elect Engn, KACST TIC RF & Photon E Soc, Riyadh, Saudi Arabia
[4] KACST, CCES, Riyadh, Saudi Arabia
关键词
METRIC-SPACES; PREDICTION; ILAE; LONG;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that is computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.
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页数:6
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