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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.


A three-level filter bank for discrete wavelet decomposition.
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Figure 1: A three-level filter bank for discrete wavelet decomposition.

Mentions: One of the most reliable digital implementation methods of wavelet transforms is based on a pyramidal algorithm called discrete wavelet transform (DWT) and developed by Mallat (1989). This method is based on a filter bank decomposition on an orthogonal base realized by a convolution between an original signal x[n] and two filters: an high-pass filter (G) which computes dj[n] (so-called “wavelet detail coefficient”) at decomposition level j; a low-pass filter (H) which computes aj[n] (so-called “wavelet approximation coefficient”) at decomposition level j (see Figure 1).


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

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

A three-level filter bank for discrete wavelet decomposition.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: A three-level filter bank for discrete wavelet decomposition.
Mentions: One of the most reliable digital implementation methods of wavelet transforms is based on a pyramidal algorithm called discrete wavelet transform (DWT) and developed by Mallat (1989). This method is based on a filter bank decomposition on an orthogonal base realized by a convolution between an original signal x[n] and two filters: an high-pass filter (G) which computes dj[n] (so-called “wavelet detail coefficient”) at decomposition level j; a low-pass filter (H) which computes aj[n] (so-called “wavelet approximation coefficient”) at decomposition level j (see Figure 1).

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.