Issue 10 Volume 41 May 16, 2003

20030032949 Medical Coll. of Pennsylvania, Philadelphia, PA
Quantization of Motor Activity into Primitives and Time-Frequency Atoms Using Independent Component Analysis
and Matching Pursuit Algorithms
Giszter, Simon F.; Oct 2001; 5 pp.; In English; Original contains color illustrations
Report No.(s): AD-A410416; No Copyright; Avail: CASI; A01, Hardcopy
It has been proposed that the segmental spinal nervous system may organize movement using a collection of force-field
primitives. The temporal organization of primitives has not been examined in detail. Recent data examining muscle activity
underlying corrections of motor patterns suggested that primitives might be recruited into motor programs as waveforms with
a constant duration. Here we test the idea that each primitive or premotor drive comprising part of the motor patterns might
be expressed as the combination of a small number of time-frequency atoms from some orthonormal basis. We analyze the
temporal organization of pre-motor drives extracted from the motor pattern by the Bell-Sejnowski algorithm for independent
component analysis. We then use matching pursuit cosine packet analysis to examine the time series of the activation
waveforms of each of the independent components. The analysis confirms that the motor pattern can be described as a
combination of a small number of time-frequency atoms. These atoms combine to generate the temporal structure and
activation of the individual components or premotor drives that generate individual muscle activity.
DTIC
Factor Analysis; Muscular Function
20030033060 Universite Catholique de Louvain, Belgium
Automatic Fibrosis Quantification By Using a k-NN Classificator
Romero, E.; Raymackers, J. M.; Macq, B.; Cuisenaire, O.; October 25, 2001; 5 pp.; In English; Original contains color
illustrations
Report No.(s): AD-A410548; No Copyright; Avail: CASI; A01, Hardcopy
This work presents an automatic algorithm to measure fibrosis in muscle sections of mdx mice, a mutant species used as
a model of the Duchenne dystrophy. The algorithm described herein automatically segments three different tissues: Muscle
cell tissue (MT), Pure collagen fiber deposit (CD) and cellular infiltrates surrounded by loose collagen deposit (CI), by using
a statistical classifier based on the k-Nearest Neighbour (k-NN) decision rule in the RGB color space. The algorithm is trained
by selecting a number of correctly classified pixels from each class. The k-NN rule classifies other pixels in the class that is
most represented among the k nearest training samples in the RGB space, which is efficiently implemented with a fast
k-distance transform algorithm. All extracted areas are quantified in absolute (micrometer squared) and relative (%) values.
For validation of this method, the different tissues were manually segmented and their qualifications statistically compared
with those obtained automatically. Statistical analysis showed interoperator variability in manual segmentation. Automatic
qualifications of the same areas did not differ significantly from their mean manual evaluations. In conclusion, this method
produce fast, reliable and reproducible results.
DTIC
Algorithms; Classifiers; Fibrosis; Statistical Analysis; Automatic Control
20030033850 Tongji Univ., Shanghai, China
A Distinguish Method of Epileptic EEG and Deglutition EEG Based on Chaotic Noise-Reduction
Ouyang, Guanghua; Li, Chunyan; Jiang, Guotai; October 25, 2001; 5 pp.; In English
Report No.(s): AD-A410230; No Copyright; Avail: CASI; A01, Hardcopy
A new method for recovering epileptic EEG and deglutition EEG’s nonnoise trajectory and distinguishing these two
waveforms is presented. The main aim of this paper is to introduce the theory and establish math model of a simple recovering
EEG’s nonnoise trajectory. This method is finally used to experiment. Our results prove that chaotic dynamics does exist in
EEG and signal-noise of EEG marked improves. Furthermore, it is also successful to recover structural characteristic of
strange attractor destroyed by noise. The paper obtains the real chaotic trajectory of EEG with which it can calculate dipole
of EEG. According to the different parameters of dipole, a method of distinguishing epileptic EEG and deglutition EEG using
the measurement of nonlinear dynamics is obtained.
DTIC
Electroencephalography; Mathematical Models; Noise Reduction; Waveforms; Chaos
20030033872 Illinois Univ., Chicago, IL, USA
Automated Quantitation of Non-Steady Flow and Lumen Area Based on Temporal Correlation
Lee, Sang H.; Alperin, Noam; October 25, 2001; 5 pp.; In English; Original contains color illustrations
Report No.(s): AD-A410357; No Copyright; Avail: CASI; A01, Hardcopy
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