Biosensor method and system based on feature vector extraction

• US 8,158,377 B2
• Filed: 08/31/2005
• Issued: 04/17/2012
• Est. Priority Date: 08/31/2005
• Status: Expired due to Fees

• Current Assignee: University of Tennessee Research Foundation (University of Tennessee), UT-Battelle LLC
• Original Assignee: UT-Battelle LLC
• Inventors: Greenbaum, Elias, Rodriguez, Miguel Jr., Qi, Hairong, Wang, Xiaoling
• Primary Examiner(s): Ware, Debbie K
• Application Number:
  US11/216,282
• Publication Number:
  US 20070105184A1
• Time in Patent Office: 2,421 Days
• Field of Search: None
• US Class Current: 435/29
• CPC Class Codes: C12Q 1/025 for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics antimicrobial activity C12Q1/18

Abstract

A method of biosensor-based detection of toxins comprises the steps of providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

Claims

1. A method of using a biosensor for identifying toxins with at least one processor, comprising the steps of:

a) obtaining at least one time-dependent control signal generated by the biosensor, the biosensor comprising a cell including naturally-occurring, free-living, indigenous photosynthetic organisms, wherein the at least one time-dependent control signal comprises a fluorescence induction signal emitted by the photosynthetic organisms in water without the presence of one or more toxins selected from chemical, biological or radiological agents;

b) obtaining a time-dependent biosensor signal that comprises a fluorescence induction signal emitted by the photosynthetic organisms in water and in a presence of the one or more toxins selected from chemical, biological or radiological agents;

c) processing, with said at least one processor, said time-dependent biosensor signal to extract statistical features from multi-level wavelet coefficients obtained from wavelet coefficient analysis of said time-dependent biosensor signal;

d) determining, with said at least one processor, at least one parameter relating to toxicity of said water from said extracted statistical features of wavelet coefficients by comparing with said at least one time-dependent control signal;

e) identifying, with said at least one processor, which of said chemical, biological or radiological agents are present in said water from said determined at least one parameter; and

f) eliminating, through said identifying, false-positives resulting from the photo-inhibition effect.

2. The method of claim 1, wherein said statistical features obtained in step c) are selected from the group consisting of the mean, the variance, the energy, and combinations thereof.

3. The method of claim 1, wherein said statistical features obtained in step c) comprise the mean, the variance and the energy.

4. The method of claim 1, wherein the multilevel wavelet coefficients comprise first-level wavelet coefficients and second-level wavelet coefficients.

5. The method of claim 4, wherein said statistical features obtained in step c) are selected from the group consisting of the mean, the variance, the energy, and combinations thereof.

6. The method of claim 4, wherein said statistical features obtained in step c) comprise the mean, the variance and the energy.

7. The method of claim 1, wherein extracting statistical features from the multi-level wavelet coefficients in step c) comprises extracting statistical features from the group consisting of first-level wavelet coefficients, second-level wavelet coefficients, third-level wavelet coefficients, fourth-level wavelet coefficients, and combinations thereof.

8. The method of claim 7, wherein said statistical features obtained in step c) are selected from the group consisting of the mean, the variance, the energy, and combinations thereof.

9. The method of claim 7, wherein said statistical features obtained in step c) comprise the mean, the variance and the energy.

10. The method of claim 1, wherein step c) of processing said time-dependent biosensor signal further comprises extracting statistical measurements from said time-dependent biosensor signal using amplitude statistics.

11. The method of claim 1, wherein step c) of processing said time-dependent biosensor signal further comprises extracting statistical measurements from said time-dependent biosensor signal using amplitude statistics;

and said extracting statistical features from wavelet coefficients step further comprising extracting statistical features from first-level wavelet coefficients, wherein said statistical features are selected from the group consisting of the mean, the variance, the energy, and combinations thereof.

12. A method of using a biosensor for identifying toxins with at least one processor, comprising the steps of:

a) obtaining at least one time-dependent control signal generated by the biosensor, the biosensor comprising a cell including naturally-occurring, free-living, indigenous photosynthetic organisms, wherein the at least one time-dependent control signal comprises a fluorescence induction signal emitted by the photosynthetic organisms in water without the presence of one or more toxins selected from chemical, biological or radiological agents;

b) obtaining a time-dependent biosensor signal that comprises a fluorescence induction signal emitted by the photosynthetic organisms in water and in a presence of the one or more toxins selected from chemical, biological or radiological agents;

c) processing, with said at least one processor, said time-dependent biosensor signal to calculate time dependent amplitude statistics of said time-dependent biosensor signal, wherein said time dependent amplitude statistics comprise a summation of time points on said time-dependent biosensor signal of at least three orders;

d) comparing, with said at least one processor, said calculated amplitude statistics with said at least one time-dependent control signal; and

e) identifying and determining a concentration for, which of said chemical, biological or radiological agents are present in said water with said at least one processor, wherein the identification and determination are based on the comparison of said calculated amplitude statistics with said at least one time-dependent control signal.

13. The method of claim 12 further comprising:

f) processing, with said at least one processor, said time-dependent biosensor signal to extract statistical features from wavelet coefficients obtained from wavelet coefficient analysis of said time-dependent biosensor signal;

wherein the comparison further comprises comparing the extracted statistical features from wavelet coefficients with said at least one time-dependent control signal;

further wherein the identification comprises the comparison of the extracted statistical features from wavelet coefficients with said at least one time-dependent control signal.

14. The method of claim 12 wherein said at least three orders comprise one of a mean, a standard deviation, and a skewness.

15. The method of claim 14 wherein said time dependent amplitude statistics comprise a summation of time points on said time-dependent biosensor signal of four orders, wherein the fourth order comprises kurtosis.