@inproceedings{b913df15c0894062843ed02eb91ede9b,
title = "Analyte detection using an ion-channel sensor array",
abstract = "Ion-channel sensors can be used for detecting small metal ions and organic molecules. The sensor consists of a chamber with a lipid bilayer hosting ion channels produced by protein insertion. These channels allow selective transport and produce a characteristic signal across the chamber for each analyte. A four chamber ion channel sensor array is built for accurate analyte detection. In this paper, we address the case in which non-uniform number of channels formed in each chamber. The power distribution information in the transform domain is used as features for each chamber signal. We employ support vector regression to estimate the number of channels inserted in each chamber and normalize the chamber signal features. The change observed in the normalized features of the chamber containing the analyte with respect to other chambers is used for detection. Results show high accuracy rates for detection of analyte using simulated data and experimental data.",
keywords = "Ion-channel, Support Vector Regression, Wavelet kernel",
author = "Sattigeri, {P. S.} and Ramamurthy, {K. N.} and Thiagarajan, {J. J.} and Michael Goryll and Andreas Spanias and Trevor Thornton",
year = "2011",
doi = "10.1109/ICDSP.2011.6004913",
language = "English (US)",
isbn = "9781457702747",
series = "17th DSP 2011 International Conference on Digital Signal Processing, Proceedings",
booktitle = "17th DSP 2011 International Conference on Digital Signal Processing, Proceedings",
note = "17th International Conference on Digital Signal Processing, DSP 2011 ; Conference date: 06-07-2011 Through 08-07-2011",
}