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Wavelet transform for real-time detection of action potentials in neural signals.

Quotb A, Bornat Y, Renaud S - Front Neuroeng (2011)

Bottom Line: We evaluate detection rate and implementation cost for detection functions dynamically comparing a signal with an adaptive threshold proportional to its SD, where the signal is the raw neural signal, respectively: (i) non-processed; (ii) processed by a DWT; (iii) processed by a SWT.We also use different mother wavelets and test different data formats to set an optimal compromise between accuracy and silicon cost.Simulation results show that for on-line AP detection implemented on a configurable digital integrated circuit, APs underneath the noise level can be detected using SWT with a well-selected mother wavelet, combined to an adaptive threshold.

View Article: PubMed Central - PubMed

Affiliation: IMS Laboratory, UMR 5218 CNRS, Polytechnic Institute of Bordeaux, University of Bordeaux Talence, France.

ABSTRACT
We present a study on wavelet detection methods of neuronal action potentials (APs). Our final goal is to implement the selected algorithms on custom integrated electronics for on-line processing of neural signals; therefore we take real-time computing as a hard specification and silicon area as a price to pay. Using simulated neural signals including APs, we characterize an efficient wavelet method for AP extraction by evaluating its detection rate and its implementation cost. We compare software implementation for three methods: adaptive threshold, discrete wavelet transform (DWT), and stationary wavelet transform (SWT). We evaluate detection rate and implementation cost for detection functions dynamically comparing a signal with an adaptive threshold proportional to its SD, where the signal is the raw neural signal, respectively: (i) non-processed; (ii) processed by a DWT; (iii) processed by a SWT. We also use different mother wavelets and test different data formats to set an optimal compromise between accuracy and silicon cost. Detection accuracy is evaluated together with false negative and false positive detections. Simulation results show that for on-line AP detection implemented on a configurable digital integrated circuit, APs underneath the noise level can be detected using SWT with a well-selected mother wavelet, combined to an adaptive threshold.

No MeSH data available.


Three-level SWT filter bank and Filter coefficients up-sampling.
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Related In: Results  -  Collection

License
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Figure 3: Three-level SWT filter bank and Filter coefficients up-sampling.

Mentions: Stationary wavelet transform (SWT) solves that issue by omitting the DWT down-sampling decimation. The approximation and the detail outputs of each level of SWT contain the same number of samples as the input. Figure 3 represents the digital SWT filter bank.


Wavelet transform for real-time detection of action potentials in neural signals.

Quotb A, Bornat Y, Renaud S - Front Neuroeng (2011)

Three-level SWT filter bank and Filter coefficients up-sampling.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3139942&req=5

Figure 3: Three-level SWT filter bank and Filter coefficients up-sampling.
Mentions: Stationary wavelet transform (SWT) solves that issue by omitting the DWT down-sampling decimation. The approximation and the detail outputs of each level of SWT contain the same number of samples as the input. Figure 3 represents the digital SWT filter bank.

Bottom Line: We evaluate detection rate and implementation cost for detection functions dynamically comparing a signal with an adaptive threshold proportional to its SD, where the signal is the raw neural signal, respectively: (i) non-processed; (ii) processed by a DWT; (iii) processed by a SWT.We also use different mother wavelets and test different data formats to set an optimal compromise between accuracy and silicon cost.Simulation results show that for on-line AP detection implemented on a configurable digital integrated circuit, APs underneath the noise level can be detected using SWT with a well-selected mother wavelet, combined to an adaptive threshold.

View Article: PubMed Central - PubMed

Affiliation: IMS Laboratory, UMR 5218 CNRS, Polytechnic Institute of Bordeaux, University of Bordeaux Talence, France.

ABSTRACT
We present a study on wavelet detection methods of neuronal action potentials (APs). Our final goal is to implement the selected algorithms on custom integrated electronics for on-line processing of neural signals; therefore we take real-time computing as a hard specification and silicon area as a price to pay. Using simulated neural signals including APs, we characterize an efficient wavelet method for AP extraction by evaluating its detection rate and its implementation cost. We compare software implementation for three methods: adaptive threshold, discrete wavelet transform (DWT), and stationary wavelet transform (SWT). We evaluate detection rate and implementation cost for detection functions dynamically comparing a signal with an adaptive threshold proportional to its SD, where the signal is the raw neural signal, respectively: (i) non-processed; (ii) processed by a DWT; (iii) processed by a SWT. We also use different mother wavelets and test different data formats to set an optimal compromise between accuracy and silicon cost. Detection accuracy is evaluated together with false negative and false positive detections. Simulation results show that for on-line AP detection implemented on a configurable digital integrated circuit, APs underneath the noise level can be detected using SWT with a well-selected mother wavelet, combined to an adaptive threshold.

No MeSH data available.