VPRS模型在麻醉EEG分析中的特征选择与决策规则应用
需积分: 9 41 浏览量
更新于2024-07-31
收藏 358KB PDF 举报
本文主要探讨了一种适应性变精度粗糙集方法（Adapted Variable Precision Rough Set Approach, AVPRS）在脑电图（Electroencephalogram, EEG）分析中的应用。该研究由Michael Ningler、Gudrun Stockmann、Gerhard Schneider、Hans-Dieter Koch和Eberhard Koch等人共同完成，他们分别来自慕尼黑工业大学麻醉科和杜伊斯堡埃森大学物流研究所。
在医疗领域尤其是人工智能医学应用中，粗糙集理论（Rough Set Theory, RST）因其强大的特征选择能力和决策规则生成能力而受到广泛关注。传统的粗糙集方法主要用于减少冗余特征，形成简洁的决策规则，但在处理实际医学数据时，如EEG信号分析，可能存在噪声和不一致性问题。为了解决这些问题，研究人员引入了变精度粗糙集模型（Variable Precision Rough Set Model, VPRS model），它作为原始粗糙集方法的扩展，能够更好地容忍数据的不确定性。
AVPRS模型的优势在于其对数据精确度的灵活性，可以根据数据质量动态调整粗糙集的精度参数，从而提高在处理复杂、噪声大或存在不一致性的EEG数据时的性能。通过这种模型，作者试图开发一种有效的策略来筛选出对 EEG 分析关键的特征，同时创建更稳健的决策规则，以支持麻醉过程中的实时监测或疾病诊断。
本文的研究目标是将AVPRS应用于麻醉过程中的EEG数据分析，以提高决策的准确性和鲁棒性，特别是在面对诸如脑电波异常、麻醉深度评估等临床挑战时。研究者首先对数据进行了预处理，然后利用AVPRS进行特征选择，并基于选定的特征构建分类规则。通过与传统粗糙集方法的比较，结果显示了AVPRS在处理EEG数据时的优越性，尤其是在处理噪声和不一致性方面。
这篇文章的主要贡献在于提出并验证了一种针对EEG数据的适应性变精度粗糙集方法，这对于改善基于脑电图数据的医疗决策系统具有重要的实际意义。通过这种方法，医生和研究人员可以更有效地分析复杂的神经活动信号，提升医疗决策的科学性和准确性。
reduct, even when there is only a slight inconsis-
tency in the classification. Therefore, more attri-
butes have to be added to B to separate the
misclassified object(s) from the others. As a result,
relative reducts from noisy decision systems contain
many condition attributes to cover the given classi-
fication for all objects of the positive region. More-
over, a high number of re lative reducts can be found
and these relative reducts are not stable when new
objects are added to the decision system.
To overcome these drawbacks, Ziarko [21] devel-
oped an extension of the RST, the variable precision
rough set model (VPRS model). The basic idea of this
model is to allow a small, previously defined classi-
fication error. Some fundamentals of the VPRS
model are introduced in the following.
Let X be a d-elementary set and Y a B-elementary
set (B C). The relative classification error c(Y, X)is
defined as:
cðY; XÞ¼1
jY \ Xj
jYj
; if jYj > 0
cðY; XÞ¼0 else:
; (5)
For a previously selected admissible classification
error b (0 b < 0.5), the b-B-lower approximation
of X is given by:
INDðBÞ
b
X ¼
[
Y 2 U=INDðBÞ : cðY; XÞbfg
The b-B-positive region of decision attribute d is
defined analogue to the definition in RST as:
POSðB; d; bÞ¼
[
X 2 U=INDðfdgÞ
INDðBÞ
b
X;
and also the degree of dependency as:
gðB;d; b Þ¼
jPOSðB; d; bÞj
jUj
:
A set of condition attributes R C is called a b-
relative reduct if [21]
1: g R; d; bðÞ¼g C; d; bðÞand (6a)
2: no attribute can be eliminated from R without
affecting the requirement 1 (6b)
Example 1. Consider the decision table presented
in Ta b le 1 . In t he RST the C-positive region is
POS(C, d)={x
6
, x
7
} and one relative reduct can
be fou nd: {c
1
, c
3
}. The calculation of the b-C-
positive region in the VPRS model with b = 0.35
yields PO S(C, d, b)={x
1
, x
2
, x
3
, x
6
, x
7
}, and
gðB; d; bÞ¼5=7. Object x
3
can be assigned to the
decision d = 1 with a relative classification error of
0.333. The objects x
4
and x
5
are still not elements
of the positive region. Condition attribute c
3
is
sufficient to assign the o bjects of the b-C-positive
region to a decision concerning the admissible
classification error.
Note, that the positive region in VPRS model
comprises not only the objects that can be classified
correctly, but also all objects of C-elementary sets
that can be classified with respect to the admissible
classification error b.
A common way to apply the VPRS model to a
decision system is — after selection of a value for b —
to replace the decisions for the C-elementary sets
which do not belong to the positive region according
to the RST (e.g. [22]): If an elementary set Y can be
assigned to a decision with a relative classification
error smaller than b, then the values of the decision
attribute are set to the same decision for all objects
of Y. In this case, the objects of Yare elements of the
C-b-positive region according to the VPRS model.
Otherwise, these objects are marked by setting the
decision to ‘‘BND’’ indicating the boundary region.
As a result, the previous probabilistic decision
table is turned into a deterministic one and can be
handled with the methods of the original RST. For
b = 0.35 the Table 1 turns to the decision table
presented in Table 2.
All equations of the RST hold for this new decision
table and all methods can be applied without any
restrictions. Relative core, relative reducts and
decision rules can be calculated as descri bed in
the previous section. The idea of the VPRS model
is applied by performing a pre-processing step
before carrying out original RST metho ds.
This approach of the VPRS model was applied to
several application areas and published in several
papers, e.g. [18,23,24].
Adapted variable precision rough set approach for EEG analysis 243
Table 1 Decision table to compare relative reducts
according to RST and VPRS model.
Uc
1
c
2
c
3
d
x
1
1121
x
2
1121
x
3
1122
x
4
1231
x
5
1232
x
6
2112
x
7
2121
Table 2 Decision table with changed class informa-
tion by VPRS model.
Uc
1
c
2
c
3
d
new
x
1
1121
x
2
1121
x
3
1121
x
4
1 2 3 BND
x
5
1 2 3 BND
x
6
2112
x
7
2121
剩余22页未读，继续阅读
2018-10-13 上传
208 浏览量
239 浏览量
2024-02-11 上传
2009-06-11 上传
133 浏览量
110 浏览量
2021-06-01 上传
