Discrimination of Vascular Conditions Using a Probabilistic Neural Network
Akira Sakane*, Toshio Tsuji*, Noboru Saeki**,
and Masashi Kawamoto**
*Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan
**Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
This paper proposes a new method to discriminate vascular conditions from changes of biological signals and arterial wall impedance using a neural network. Since strong individual differences cause difficulty in discriminating the vascular conditions, we introduce an impedance ratio of the arterial wall and attempt to discriminate the vascular conditions during surgical operations. From experimental results, it is shown that various stimulations during operations cause changes in the impedance parameters of the arterial wall, and vascular conditions such as vasodilation, vasoconstriction, and shock can be discriminated accurately using the proposed method. This method will be useful for monitoring the vascular conditions during operations.
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