(reading group) automatic detection of action potentials in a noisy neural recording
DESCRIPTION
Reading Group Activity - October 2013 B31XM Advanced Image Analysis Module Heriot-Watt University VIBOT Promotion 7 (2012-2014)TRANSCRIPT
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Automatic Detection of Action
Potentials in a Noisy Neural
Recording
I. Sadek, M. Elawady
Supervisor: Dr. Mathini Sellathurai
1B31XM Advanced Image Analysis
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2B31XM Advanced Image Analysis
Spike Detection and Clustering With Unsupervised
Wavelet Optimization in Extracellular Neural
RecordingsVahid Shalchyan, Winnie Jensen and Dario Farina
IEEE Trans. Biomed. Engineering, 59(9):2576-2585,
2012.
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Agenda
• Overview
• Related Work
• Methodology
• Results
• Conclusion
B31XM Advanced Image Analysis 3
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Agenda
• Overview
• Related Work
• Methodology
• Results
• Conclusion
B31XM Advanced Image Analysis 4
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Overview
B31XM Advanced Image Analysis 5
Action Potential (AP)
A series of changes result from applying an electric stimulation to excitable tissues
(i.e. nerves, all types of muscle).
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Overview
B31XM Advanced Image Analysis 6
Problem Definition
The signals acquired from the microelectrodes are contaminated by background
noise
Noisy Simulated APs
Filtered Simulated APs
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Agenda
• Overview
• Related Work
• Methodology
• Results
• Conclusion
B31XM Advanced Image Analysis 7
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Related Work
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Methods Pros Cons
Amplitude Thresholding Low computational load
• Threshold selection for a tradeoff
between false negatives (FNs)
and false positives (FPs)
• Failed when spike amplitude are
close to or lower than the
background noise
Template Matching High detection performanceSpike shape knowledge are
required
Nonlinear Energy Operator
(NEO)
& Multi-resolution
Teager Energy Operator
(MTEO)
Easy implementation and
computational simplicitySame as Amplitude Thresholding
Wavelet Transformation
If wavelet shape is selected
properly, the wavelet transform can
be seen as a bank of matched filters
Prior knowledge about spike
shapes are required
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Agenda
• Overview
• Related Work
• Methodology
• Results
• Conclusion
B31XM Advanced Image Analysis 9
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Methodology
B31XM Advanced Image Analysis 10
Wavelet Transform
Wavelets are defined by two primary functions :
•Wavelet function (mother wavelet) ψ(t)
•Scaling function (father wavelet) φ(t)
where a is scalar factor and b is translation factor
Haar Wavelet Transform
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Methodology
B31XM Advanced Image Analysis 11
Stationary Wavelet Transform (SWT)
DWT SWT
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Methodology
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Wavelet Parameterization
Filter length = 4One independent parameter (α)
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Methodology
B31XM Advanced Image Analysis 13
Flowchart
Noisy Signals
Filtered Signals
Detection(SWT)
Clustering(DWT)
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Methodology
B31XM Advanced Image Analysis 14
Flowchart
Noisy Signals
Filtered Signals
Detection(SWT)
Clustering(DWT)
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Methodology
B31XM Advanced Image Analysis 15
Detection
SWT five levels decomposition
Hard thresholding
Three maximum energy scale
selection
Summation & Filtering
Selection Criteria I
Final AP Candidates
AP Candidates
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Methodology
B31XM Advanced Image Analysis 16
Detection
SWT five levels decomposition
Hard thresholding
Three maximum energy scale
selection
Summation & Filtering
Selection Criteria I
Final AP Candidates
AP Candidates
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Methodology
B31XM Advanced Image Analysis 17
Detection
SWT five levels decomposition
Hard thresholding
Three maximum energy scale
selection
Summation & Filtering
Selection Criteria I
Final AP Candidates
AP Candidates
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Methodology
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Detection – Thresholding I
Median Absolute Deviation (MAD) Operator
Example:
• Consider the data (1, 1, 2, 2, 4, 6, 9).
• It has a median value of 2.
• The absolute deviations about 2 are (1, 1, 0, 0, 2, 4, 7).
• The sorted absolute deviations are (0, 0, 1, 1, 2, 4, 7).
• So the median absolute deviation (MAD) for this data is 1
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Methodology
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Detection – Thresholding II
Threshold level at each scale is computed as follows:
Where N is the number of samples (n) and σj is the noise standard
deviation at scale j which is estimated with (MAD) operator
80% of this threshold level are used to keep the highest 20% candidates
for detection
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Methodology
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Detection – Thresholding III
Hard thresholding can be described as follows:
wavelet coefficient after thresholding at scale j
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Methodology
B31XM Advanced Image Analysis 21
Detection
SWT five levels decomposition
Hard thresholding
Three maximum energy scale
selection
Summation & Filtering
Selection Criteria I
Final AP Candidates
AP Candidates
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Methodology
B31XM Advanced Image Analysis 22
Detection – Energy Selection
The signal energy at each scale (Ewj) is calculated as
Wavelet coefficient after thresholding at scale j
Average value at each scale
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Methodology
B31XM Advanced Image Analysis 23
Detection
SWT five levels decomposition
Hard thresholding
Three maximum energy scale
selection
Summation & Filtering
Selection Criteria I
Final AP Candidates
AP Candidates
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Methodology
B31XM Advanced Image Analysis 24
Detection – Summation & Filtering
S(n) is calculated as the summation of the absolute values of the wavelet
coefficients
Wavelet coefficient after thresholding at scale j
for removing flase peaks, S(n) is filtered with smoothing window W(n)
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Methodology
B31XM Advanced Image Analysis 25
Detection
SWT five levels decomposition
Hard thresholding
Three maximum energy scale
selection
Summation & Filtering
Selection Criteria
Final AP Candidates
AP Candidates
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Methodology
B31XM Advanced Image Analysis 26
Detection – Selection Criteria
The optimal wavelet basis selection is based on the correlation similarity
(wave form x(n) and wave form y(n))
Where E is the expected value operator
Designated label for i(n) Median value of APs
KD = 0.4 Rejects very far outliers
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Methodology
B31XM Advanced Image Analysis 27
Flowchart
Noisy Signals
Filtered Signals
Detection(SWT)
Clustering(DWT)
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Methodology
B31XM Advanced Image Analysis 28
Clustering
DWT five levels decomposition
ClusteringSelection Criteria II
Final Classified APs
Classified APs
Final AP Candidates
Based on normal distance
measurement
KC = 0.8 represents the high similarity of shapes
between APs
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Agenda
• Overview
• Related Work
• Methodology
• Results
• Conclusion
B31XM Advanced Image Analysis 29
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Results
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Detector Output
a-band pass filtered data b-THR detector c-NEO detector
d-MTEO detector e-DWT product detector f-Proposed method
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Results
B31XM Advanced Image Analysis 31
Comparison of average TPR vs SNR
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Results
B31XM Advanced Image Analysis 32
Detection Performance
1st
2nd
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Agenda
• Overview
• Related Work
• Methodology
• Results
• Conclusion
B31XM Advanced Image Analysis 33
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Conclusion
B31XM Advanced Image Analysis 34
• Introduce unsupervised optimization for the best
basis selection of detection & clustering APs.
• Improve the spike sorting performance by applying
unsupervised criterion based on the correlation
similarity.
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References
B31XM Advanced Image Analysis 35
• Rieder, P.; Gerganoff, K.; Gotze, J.; Nossek, J.A., “Parameterization and
implementation of orthogonal wavelet transforms,” Acoustics, Speech,
and Signal Processing, 1996. ICASSP-96. Conference Proceedings.,
1996 IEEE International Conference on , vol.3, no., pp.1515,1518 vol. 3,
7-10 May 1996.
• Shalchyan, V.; Jensen, W.; Farina, D., “Spike Detection and Clustering
With Unsupervised Wavelet Optimization in Extracellular Neural
Recordings,” Biomedical Engineering, IEEE Transactions on , vol.59,
no.9, pp.2576,2585, Sept. 2012.
• Zhou, X.; Zhou, C.; Stewart, B.G., “Comparisons of discrete wavelet
transform, wavelet packet transform and stationary wavelet transform in
denoising PD measurement data,” Electrical Insulation, 2006.
Conference Record of the 2006 IEEE International Symposium on , vol.,
no., pp.237,240, 11-14 June 2006.
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B31XM Advanced Image Analysis 36