H. CAO ET AL.

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of the test group than two polynomial regression models

(S and RecP) which had shown very good agreement

with ones of the learning group [6]. That may be caused

by using a combination of several measures in this model

and the combination can reduce the error gen erated by

using only one measure. Meanwhile, we introduced our

tree two non-linear measures (TTV and TTA) which are

able to evaluate the complexity of a dynamic system,

because TT represents the average time in which the

system is trapped in a specific state [9].

In our selected decision tree, the root node (in the level

1) is S (node 1) and the nodes in the level 2 are S (node 3)

and TTA (node 2). That means S and TTA are two do-

minant measures in the classification for EDSS scores.

Just as we expected, the patients could be directly classi-

fied into three principal groups using S: if S > 2149.38

mm2, EDSS score is between 3 and 4.5 (high scores); if

910.53 mm2 < S < 2149.38 mm2, EDSS is between 2 and

3 (medium scores); if S < 910.53 mm2, EDSS is between

0 and 3 (low-medium scores). Fo r the group of low-me-

dium EDSS score, w e can continue classifying them by

using TTA: if TTA < 15, EDSS ranges from 2 to 3 (me-

dium scores); otherwise, EDSS ranges fro m 0 to 2 (low

scores). From that, we have observed that S (linear meas-

ure) allows selecting the patients with high and medium

EDSS scores, while TTA (non-linear measure) allows

selecting low EDSS scores. Generally, patients with high

EDSS scores have a great S [3]. Thus, S is the most im-

portant measure to classify EDSS, especially to identify

high scores. However, it is not sufficient to distinguish

all the scores. To identify low scores, we need to take

into account TTA. When TTA is high, the COP’s instan-

taneous acceleration is trapped for much amount of time,

and its oscillation is small around an equilibrium position

(such as EDSS between 0 and 2). On the contrary, TTA

becomes lower as the variation of the acceleration in-

creases (such as EDSS between 2 and 3), because the

increase of the variation is becoming complex and sys-

tem dynamics are changing fast due to its faster COP’s

displacement over the time. As TTA plays an important

role to identify low EDSS scores between 0 and 2, it may

be a good indicator linked with S to predict the emer-

gence of MS. In addition of these two measures, L, RecP

and TTV should be considered to assess EDSS scores

within each principal grou p.

5. Conclusion

In this paper, we presented a method for assessing EDSS

score from postural data of patients with MS using a de-

cision tree with five measures (S, L, RecP, TTV and

TTA). The results have signified that our tree model with

a combination of some measures is able to automatically

assess the EDS S scores and that it is possible to distin-

guish the EDSS scores by using linear and non-linear

postural sway measures. It would be interesting to test

other values of the time d elay and the embedding dimen-

sion for RQA and to study the difference between the

sexes in the future research.

6. Acknowledgements

The authors would like to thank the Hôpital de Saint-

Philibert, Lomme, for prov iding posturologic data of the

healthy subjects and the patients with multiple sclerosis.

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