(Reading Group) Automatic Detection of Action Potentials in a Noisy Neural Recording

Automatic Detection of Action

Potentials in a Noisy Neural

Recording

I. Sadek, M. Elawady

Supervisor: Dr. Mathini Sellathurai

1B31XM Advanced Image Analysis

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.

Agenda

• Overview

• Related Work

• Methodology

• Results

• Conclusion

B31XM Advanced Image Analysis 3

Agenda

• Overview

• Related Work

• Methodology

• Results

• Conclusion


Overview


Action Potential (AP)

A series of changes result from applying an electric stimulation to excitable tissues

(i.e. nerves, all types of muscle).

Overview


Problem Definition

The signals acquired from the microelectrodes are contaminated by background

noise

Noisy Simulated APs

Filtered Simulated APs

Agenda

• Overview

• Related Work

• Methodology

• Results

• Conclusion


Related Work


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

Agenda

• Overview

• Related Work

• Methodology

• Results

• Conclusion


Methodology


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

Methodology


Stationary Wavelet Transform (SWT)

DWT SWT

Methodology


Wavelet Parameterization

Filter length = 4One independent parameter (α)

Methodology


Flowchart

Noisy Signals

Filtered Signals

Detection(SWT)

Clustering(DWT)

Methodology


Flowchart

Noisy Signals

Filtered Signals

Detection(SWT)

Clustering(DWT)

Methodology


Detection

SWT five levels decomposition

Hard thresholding

Three maximum energy scale

selection

Summation & Filtering

Selection Criteria I

Final AP Candidates

AP Candidates

Methodology


Detection


Hard thresholding


selection



Final AP Candidates

AP Candidates

Methodology


Detection


Hard thresholding


selection



Final AP Candidates

AP Candidates

Methodology


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

Methodology


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

Methodology


Detection – Thresholding III

Hard thresholding can be described as follows:

wavelet coefficient after thresholding at scale j

Methodology


Detection


Hard thresholding


selection



Final AP Candidates

AP Candidates

Methodology


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

Methodology


Detection


Hard thresholding


selection



Final AP Candidates

AP Candidates

Methodology


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)

Methodology


Detection


Hard thresholding


selection


Selection Criteria

Final AP Candidates

AP Candidates

Methodology


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

Methodology


Flowchart

Noisy Signals

Filtered Signals

Detection(SWT)

Clustering(DWT)

Methodology


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

Agenda

• Overview

• Related Work

• Methodology

• Results

• Conclusion


Results


Detector Output

a-band pass filtered data b-THR detector c-NEO detector

d-MTEO detector e-DWT product detector f-Proposed method

Results


Comparison of average TPR vs SNR

Results


Detection Performance

1st

2nd

Agenda

• Overview

• Related Work

• Methodology

• Results

• Conclusion


Conclusion


• 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.

References


• 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.
