A Wavelet-Based Method for Action Potential Detection from Extracellular Neural Signal Recording with Low Signal-to-Noise Ratio

Cited 114 time in Web of Science Cited 137 time in Scopus

Kim, Kyung Hwan; Kim, Sung June

Issue Date
Institute of Electrical and Electronics Engineers (IEEE)
IEEE Trans. Biomed. Eng., vol. 50, pp. 999-1011, Aug. 2003
Action potential detectionextracellular neural signal recordingsignal-to-noise ratioTeager energy operatorwavelet transform
We present a method for the detection of action potentials,
an essential first step in the analysis of extracellular neural
signals. The low signal-to-noise ratio (SNR) and similarity of spectral
characteristic between the target signal and background noise
are obstacles to solving this problem and, thus, in previous studies
on experimental neurophysiology, only action potentials with sufficiently
large amplitude have been detected and analyzed. In order
to lower the level of SNR required for successful detection, we propose
an action potential detector based on a prudent combination
of wavelet coefficients of multiple scales and demonstrate its performance
for neural signal recording with varying degrees of similarity
between signal and noise. The experimental data include
recordings from the rat somatosensory cortex, the giant medial
nerve of crayfish, and the cutaneous nerve of bullfrog. The proposed
method was tested for various SNR values and degrees of
spectral similarity. The method was superior to the Teager energy
operator and even comparable to or better than the optimal linear
detector. A detection ratio higher than 80% at a false alarm ratio
lower than 10% was achieved, under an SNR of 2.35 for the rat
cortex data where the spectral similarity was very high.
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