Detection of Action Potentials in the Presence of Noise Using Phase-Space Techniques
Emerging Bio-engineering fields such as Brain
Computer Interfaces, neuroprothesis devices and modeling and
simulation of neural networks have led to increased research activity
in algorithms for the detection, isolation and classification of Action
Potentials (AP) from noisy data trains. Current techniques in the field
of 'unsupervised no-prior knowledge' biosignal processing include
energy operators, wavelet detection and adaptive thresholding. These
tend to bias towards larger AP waveforms, AP may be missed due to
deviations in spike shape and frequency and correlated noise
spectrums can cause false detection. Also, such algorithms tend to
suffer from large computational expense.
A new signal detection technique based upon the ideas of phasespace
diagrams and trajectories is proposed based upon the use of a
delayed copy of the AP to highlight discontinuities relative to
background noise. This idea has been used to create algorithms that
are computationally inexpensive and address the above problems.
Distinct AP have been picked out and manually classified from
real physiological data recorded from a cockroach. To facilitate
testing of the new technique, an Auto Regressive Moving Average
(ARMA) noise model has been constructed bases upon background
noise of the recordings. Along with the AP classification means this
model enables generation of realistic neuronal data sets at arbitrary
signal to noise ratio (SNR).
[1] R. I. Curry, "Development and Modeling of a versatile Active Micro-
Electrode Array for high density in vivo and in vitro neural signal
investigation" PhD Thesis University of Durham 2008, pp. 154-168.
[2] M. Rizk, "A single-chip signal processing and telemetry engine for an
implantable 96-channel neural data acquisition system" IOP J. Neural
Engineering, vol. 4, pp. 309-321, July 07.
[3] R. R. Harrison, "A low power integrated circuit for a wireless 100-
electrode neural recording system" IEEE J. Solid-State Circuits, vol.
41(1), pp. 123-133, Jan 07.
[4] S. Hafizovic, "A CMOS-based microelectrode array for interaction with
neuronal cultures" ELSEVIER J. Neuroscience Methods, vol 164, pp.
93-106, 07.
[5] K. K. Shyu, "Implementation of Pipelined FastICA on FPGA for realtime
blind source separation" IEEE Trans on Neural Networks, vol.
19(6), pp. 958-970, June 08.
[6] A. M. Kamboh, "Area-Power Efficient VLSI Implementation of
Multichannel DWT for Data Compression in Implantable
Neuroprosthetics" IEEE Trans on Biomedical Circuits and Systems, vol.
1(2), pp. 128-135, June 07.
[7] H. L. Chan, "Detection of neuronal spikes using an adaptive threshold
based on the max-min spread sorting method" ELSEVIER J.
Neuroscience Methods, vol. 172, pp.112-121, 08.
[8] N. Mtetwa, "Smoothing and thresholding in neuronal spike detection"
ELSEVIER Neurocomputing, vol. 69, pp.1366-1370, 06.
[9] K. H. Kim, "Neural Spike Sorting under nearly 0-db Signal-to-Noise
Ratio using Nonlinear Energy Operator and Artificial Neural-Network
Classifier" IEEE Trans. On Biomedical Engineering, vol. 47(10), pp.
1406-1411, Oct 00.
[10] J. H. Choi, "Neural action potential detector using multi-resolution
TEO" IEEE Electronic Letters, vol. 38(12), pp. 541-543, June 02.
[11] J. H. Choi, A new action potential detector using the MTEO and its
effects on spike sorting systems at low signal-to-noise ratios" IEEE
Trans. on Biomedical Engineering, vol. 53(4), pp.738-746, Apr 06.
[12] K. H. Kim, "A wavelet-based method for action potential detection from
extracellular Neural Signal Recording with low signal-to-noise ratio"
IEEE Trans. on Biomedical Engineering, vol. 50(8), pp. 999-1011, Aug
03.
[13] P. Celka, "Noise Reduction in Rhythmic and Multitrial Biosignals With
Applications to Event-Related Potentials" IEEE Trans. on Biomedical
Engineering, vol. 55(7), pp.1809-1821, July 08.
[14] L. Citi, "On the use of wavelet denoising and spike sorting techniques to
process electroneurographic signals recorded using intraneural
electrodes" ELSEVIER J. Neuroscience Methods, vol. 172, pp. 294-302,
08.
[15] H. L. Chan, "Classification of neuronal spikes over the reconstructed
phase space" ELSEVIER J. Neuroscience Methods, vol. 168, pp. 203-
211, 08.
[16] T. I. Aksenova, "An unsupervised automatic method for sorting neuronal
spike waveforms in awake and freely moving animals" ELSEVIER
Methods, vol. 30, pp. 178-187, 03.
[17] R. Escola, "SIMONE: A realistic neural network simulator to reproduce
MEA-based recordings" IEEE Trans. on Neural Systems and
Rehabilitation Engineering, vol. 16(2), pp.149-160, Apr 08.
[18] L. S. Smith, "A tool for synthesizing spike trains with realistic
interference" ELSEVIER J. Neuroscience Methods, vol. 159, pp. 170-
180, 07.
[1] R. I. Curry, "Development and Modeling of a versatile Active Micro-
Electrode Array for high density in vivo and in vitro neural signal
investigation" PhD Thesis University of Durham 2008, pp. 154-168.
[2] M. Rizk, "A single-chip signal processing and telemetry engine for an
implantable 96-channel neural data acquisition system" IOP J. Neural
Engineering, vol. 4, pp. 309-321, July 07.
[3] R. R. Harrison, "A low power integrated circuit for a wireless 100-
electrode neural recording system" IEEE J. Solid-State Circuits, vol.
41(1), pp. 123-133, Jan 07.
[4] S. Hafizovic, "A CMOS-based microelectrode array for interaction with
neuronal cultures" ELSEVIER J. Neuroscience Methods, vol 164, pp.
93-106, 07.
[5] K. K. Shyu, "Implementation of Pipelined FastICA on FPGA for realtime
blind source separation" IEEE Trans on Neural Networks, vol.
19(6), pp. 958-970, June 08.
[6] A. M. Kamboh, "Area-Power Efficient VLSI Implementation of
Multichannel DWT for Data Compression in Implantable
Neuroprosthetics" IEEE Trans on Biomedical Circuits and Systems, vol.
1(2), pp. 128-135, June 07.
[7] H. L. Chan, "Detection of neuronal spikes using an adaptive threshold
based on the max-min spread sorting method" ELSEVIER J.
Neuroscience Methods, vol. 172, pp.112-121, 08.
[8] N. Mtetwa, "Smoothing and thresholding in neuronal spike detection"
ELSEVIER Neurocomputing, vol. 69, pp.1366-1370, 06.
[9] K. H. Kim, "Neural Spike Sorting under nearly 0-db Signal-to-Noise
Ratio using Nonlinear Energy Operator and Artificial Neural-Network
Classifier" IEEE Trans. On Biomedical Engineering, vol. 47(10), pp.
1406-1411, Oct 00.
[10] J. H. Choi, "Neural action potential detector using multi-resolution
TEO" IEEE Electronic Letters, vol. 38(12), pp. 541-543, June 02.
[11] J. H. Choi, A new action potential detector using the MTEO and its
effects on spike sorting systems at low signal-to-noise ratios" IEEE
Trans. on Biomedical Engineering, vol. 53(4), pp.738-746, Apr 06.
[12] K. H. Kim, "A wavelet-based method for action potential detection from
extracellular Neural Signal Recording with low signal-to-noise ratio"
IEEE Trans. on Biomedical Engineering, vol. 50(8), pp. 999-1011, Aug
03.
[13] P. Celka, "Noise Reduction in Rhythmic and Multitrial Biosignals With
Applications to Event-Related Potentials" IEEE Trans. on Biomedical
Engineering, vol. 55(7), pp.1809-1821, July 08.
[14] L. Citi, "On the use of wavelet denoising and spike sorting techniques to
process electroneurographic signals recorded using intraneural
electrodes" ELSEVIER J. Neuroscience Methods, vol. 172, pp. 294-302,
08.
[15] H. L. Chan, "Classification of neuronal spikes over the reconstructed
phase space" ELSEVIER J. Neuroscience Methods, vol. 168, pp. 203-
211, 08.
[16] T. I. Aksenova, "An unsupervised automatic method for sorting neuronal
spike waveforms in awake and freely moving animals" ELSEVIER
Methods, vol. 30, pp. 178-187, 03.
[17] R. Escola, "SIMONE: A realistic neural network simulator to reproduce
MEA-based recordings" IEEE Trans. on Neural Systems and
Rehabilitation Engineering, vol. 16(2), pp.149-160, Apr 08.
[18] L. S. Smith, "A tool for synthesizing spike trains with realistic
interference" ELSEVIER J. Neuroscience Methods, vol. 159, pp. 170-
180, 07.
@article{"International Journal of Medical, Medicine and Health Sciences:51362", author = "Christopher Paterson and Richard Curry and Alan Purvis and Simon Johnson", title = "Detection of Action Potentials in the Presence of Noise Using Phase-Space Techniques", abstract = "Emerging Bio-engineering fields such as Brain
Computer Interfaces, neuroprothesis devices and modeling and
simulation of neural networks have led to increased research activity
in algorithms for the detection, isolation and classification of Action
Potentials (AP) from noisy data trains. Current techniques in the field
of 'unsupervised no-prior knowledge' biosignal processing include
energy operators, wavelet detection and adaptive thresholding. These
tend to bias towards larger AP waveforms, AP may be missed due to
deviations in spike shape and frequency and correlated noise
spectrums can cause false detection. Also, such algorithms tend to
suffer from large computational expense.
A new signal detection technique based upon the ideas of phasespace
diagrams and trajectories is proposed based upon the use of a
delayed copy of the AP to highlight discontinuities relative to
background noise. This idea has been used to create algorithms that
are computationally inexpensive and address the above problems.
Distinct AP have been picked out and manually classified from
real physiological data recorded from a cockroach. To facilitate
testing of the new technique, an Auto Regressive Moving Average
(ARMA) noise model has been constructed bases upon background
noise of the recordings. Along with the AP classification means this
model enables generation of realistic neuronal data sets at arbitrary
signal to noise ratio (SNR).", keywords = "Action potential detection, Low SNR, Phase spacediagrams/trajectories, Unsupervised/no-prior knowledge.", volume = "2", number = "8", pages = "255-4", }