Commenced in January 2007
Paper Count: 30077
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).
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056699Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1307
 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.
 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.
 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.
 S. Hafizovic, "A CMOS-based microelectrode array for interaction with neuronal cultures" ELSEVIER J. Neuroscience Methods, vol 164, pp. 93-106, 07.
 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.
 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.
 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.
 N. Mtetwa, "Smoothing and thresholding in neuronal spike detection" ELSEVIER Neurocomputing, vol. 69, pp.1366-1370, 06.
 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.
 J. H. Choi, "Neural action potential detector using multi-resolution TEO" IEEE Electronic Letters, vol. 38(12), pp. 541-543, June 02.
 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.
 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.
 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.
 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.
 H. L. Chan, "Classification of neuronal spikes over the reconstructed phase space" ELSEVIER J. Neuroscience Methods, vol. 168, pp. 203- 211, 08.
 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.
 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.
 L. S. Smith, "A tool for synthesizing spike trains with realistic interference" ELSEVIER J. Neuroscience Methods, vol. 159, pp. 170- 180, 07.