Feature Extraction for MER Signals Using Adaptive Filter Banks

German Castellanos-DomınguezUniversidad Nacional de Colombia

Department of Electronic EngineeringManizales, Colombia

gcastell@telesat.com.co

Alvaro Orozco-GutierrezUniversidad Tecnologica de Pereira

Department of Electrical EngineeringPereira, Colombiaaaog@utp.edu.co

Eduardo Giraldo SuarezUniversidad Tecnologica de Pereira

Department of Electrical EngineeringPereira, Colombia

egiraldos@utp.edu.co

Abstract

A methodology for feature extraction using adaptive fil-ter banks in automatic identification case where the brainzone is presented. Proposed filter banks track in more accu-rate way any change of parameters using Teager algorithmfor energy calculation. Automatic threshold selection is ap-plied for adaptive varying filters. The classification resultsare compared with fixed filter banks and good results areobtained.

1. Introduction

Microelectrode recording (MER) is a fundamental partof many surgical procedures for movement disorders, spe-cially, to obtain better precision and physiologic validationof the nominal target location. One of the most critical chal-lenges facing neurosurgeons who perform Stereotactic neu-rosurgery is to locate the target structure. An experiencedneurophysiologist may identify different parts of the centralnervous system just by listening (by conventional speakers)or examining the time domain behavior of electrical activ-ity. Most subcortical regions, particularly the thalamus andthe basal ganglia, have specific and unique electrical dis-charge patterns that may be recognized by the trained ear ofan experience clinician or researcher [9]. Although modernsurgical workstations provide some tools for MER analysis,the techniques are cumbersome, difficult to interpret, needmanual tuning, and require the neurosurgeon to mentallykeep track of how the recordings change as the microelec-trode moves through different brain structures [1].

In order to get a better description of time domain be-havior, filter banks are proposed for MER feature extrac-tion [2], using their band frequency analysis. In particular,fixed wavelets has been applied for the analysis using filterbanks [1, 5] with a selected heuristic wavelet bases.

However, biological signal have a time variant structurethat demand feature extraction techniques that change andadapt according to the dynamical of the signal. In this sense,an important modification of the wavelet analysis allows tochange parameters like vanishing moments, which allowsfilter order changes. Proposed filter banks track in moreaccurate way any change of parameters of time-varying se-quence [7, 8, 10, 3, 4]. Nevertheless, this filter banks needsto select a thresholds for filter order changes. However, theselection of a threshold depends of the application. In [3]the thresholds are selected according to perfect reconstruc-tion criteria. In a case of a dynamic signal changes is nota good option to approach because is calculated in a fixedway and depends of the selected filters instead of the an-alyzed signal. Then, it’s necessary to develop a dynamictime varying method to select an automatic threshold thatdepends of the analyzed signal.

In this paper, we present adaptive filter banks that changeaccording to the level energy in the signal which allowsa dynamical signal processing structure. Filters frequencyrange change according to the estimated energy level. Asa result, each frequency band preserve relevant informationof the signal. Automatic threshold selection according tothe signal level energy and independent of the filter banks ispresented for a more discriminant feature space.

Besides, MER signals have high frequencies compo-nents and its use in surgical procedures demands realtime processing, calculation of filter banks through lifting

Electronics, Robotics and Automotive Mechanics Conference 2008

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DOI 10.1109/CERMA.2008.95

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schemes are discussed. This paper is organized as fol-lows. Section 2 recalls the orthogonal and biorthogonalfilter banks. Section 3 presents adaptive analysis and theadaptivity criteria for biorthogonal filters. Section 4 presentthe results for brain zone identification over MER signals.

2. Filter banks

Filter banks allows signal decomposition in frequencybands, they can be orthogonal or biorthogonal. Biorthogo-nal filters allows more freedom for filter selection. Filtersbanks can be implemented through lifting schemes whichimprove time calculation [11, 5].

2.1. Orthogonal Filter Banks

Let h[n] be a FIR filter defined by the sequence h[n] ={h[0], h[1], . . . , h[L− 1]} such that it is orthogonal to itsown translations:

〈h[n− 2k], h[n− 2l]〉 = δkl (1)

where δkl is the Kronecker delta. Let H(z) be z-transform of a lowpass filter h. Then, a highpass filter gis defined such that it is orthogonal to its own translations:

〈g[n− 2k], g[n− 2l]〉 = δkl (2)

and its satisfies that h and g are mutually orthogonal

〈h[n− 2k], g[n− 2l]〉 = 0 (3)

Therefore, orthonormal set {h[n− 2k], g[n− 2l]}k,l∈Z

is called orthonormal basis in �2. Hence, any sequence in�2 has representation

x[n] =∑k∈Z

αk h[n− 2k] +∑l∈Z

βl g[n− 2l] (4)

where the coefficients αk and βl are defined as

αk = 〈h[n− 2k], x[n]〉 , k ∈ Z (5)

βl = 〈g[n− 2l], x[n]〉 , l ∈ Z (6)

2.2. Biorthogonal Filter Banks

A couple of filters lowpass h and highpass g are definedorthogonal to its own translations, but not orthogonal be-tween them. Similarly, a lowpass filter h and a highpassfilter g with the same conditions according to:

〈h[n− 2k], h[n− 2l]〉 = δkl (7)

〈g[n− 2k], g[n− 2l]〉 = δkl (8)

and

〈h[n− 2k], g[n− 2l]〉 = 〈g[n− 2k], h[n− 2l]〉 = 0 (9)

In the orthogonal filter banks , using the dual basis set hy g and h y g, any sequence in �2 can be represented as

x[n] =∑k∈Z

αk h[n− 2k] +∑l∈Z

βl g[n− 2l] (10)

where

αk = 〈h[n− 2k], x[n]〉 , k ∈ Z (11)

βl = 〈g[n− 2l], x[n]〉 , l ∈ Z (12)

and, any sequence in �2 can be represented as

x[n] =∑k∈Z

αk h[n− 2k] +∑l∈Z

βl g[n− 2l] (13)

where

αk = 〈h[n− 2k], x[n]〉, k ∈ Z (14)

βl = 〈g[n− 2l], x[n]〉, l ∈ Z (15)

If the filters h y g are organized in a polyphase im-plementation P (z) [2], a matrix factorization can be ob-tained where the filters are extended in Si(z) y Ti(z) with1 ≤ i ≤ m and with a non zero constant K such that

P(z) =

Normalization︷ ︸︸ ︷[K 0)0 1/K

] 1∏i=m

⎧⎪⎪⎪⎨⎪⎪⎪⎩

[1 −Si(z)0 1

]︸ ︷︷ ︸

Update

Predict︷ ︸︸ ︷[1 0

−Ti(z) 1

]⎫⎪⎪⎪⎬⎪⎪⎪⎭

This factorization allows include adaptability schemesselecting in each sample the polynomials Si and Ti accord-ing to and adaptability criteria using the Teager energy inthe analysis window and changing the analysis band of thefilter. For that change a decision operator D is used. Thegeneralized form of this operator D with output dn is givenby

D =

⎧⎪⎪⎪⎪⎨⎪⎪⎪⎪⎩

dn = 0, γ0 ≤ s < γ′0

dn = 1, γ1 ≤ s < γ′1

...

dn = k, γk ≤ s < γ′k

(16)

where s depends of x[n] in the analysis window and cor-respond to the Teager energy. The selection of the thresh-olds γ is realized in an automatic form according to a cal-culation of the energy levels for each analysis window. Thiscalculation can be realized on-line or off-line using univer-sal threshold selection over the resultant energy signal foreach window analysis.

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�

�

�

�

�

��

��

�

�

�

�

�

��

�

�

↓ 2X

↓ 2z

U

Xe

Xo

DU

XL

XH

PDP

Figure 1. General adaptive lifting scheme

3. General Adaptive Lifting Scheme

Fig. 1 shows adaptive lifting scheme for filter orderchange. Signal xe is updated with the operator U (updatestep) to obtain coarse coefficients xL and signal xo is actu-alized with P operator (prediction) to obtain detail coeffi-cients xH . In this generalized lifting scheme, update opera-tors U and P are adaptive. Decision operator D depend ofthe local features of the signals xe y xo and determine thefilter changes.

For xL we use

xL[n] = xe[n] + Udn(xo[n]) . (17)

with decision operator DU where dn is the decision forsample n, y Udn is the selected filter for sample n. In asimilar way, xH is obtainde as

xH [n] = xo[n]− Pdn (xL[n]) . (18)

in this case, dn is obtained using DP defined similar toDU , with prediction operators given by Pdn

.

3.1. Adaptability criteria

A possible criteria for adaptive filter change is signal en-ergy. Energy can be calculated through many algorithmssuch as Shannon [10], and Teager [6]. In update step, Ud isthe d− th filter selected according to DU decision criteria,in which energy calculation is used as adaptability criteria.In this way, a large filter is selected if the energy changeis too slow (static signals), or a short filter is selected if theenergy change is too large (dynamic signals). Energy calcu-lation using Teager algorithm has less processing time andimprove the performance of the decision criteria D. Energycalculation for a n sample on a x array is as follows

E[n] = x[n]2 − x[n− 1]x[n + 1]

Therefore, it is possible to obtain a dynamical signal pro-cessing structure that changes according to the signal en-ergy. Maximum value and standard deviation are selectedas features over approximation and detail coefficients thatidentify analyzed signal.

Figure 2. Analysis results using generalizedadaptive lifting

Table 1. Teager resultsPattern % Tal % STN % SN % ZI

Complete signal 91.67 100.00 78.57 100.00

Background noise 83.33 58.57 44.29 30.40

Spikes 52.50 87.14 22.86 83.60

4. Results

The proposed method is applied over MER signalsdatabase for four brain zones and 35 recorders for eachzone: subthalamic nucleus (STN), thalamus (Tal), substan-tia nigra (SN), and zona incerta (ZI) of the Polytechnic Uni-versity of Valencia, Spain. An example of the application ofthe method over a STN signal is show in Fig. 2. The deci-sion criteria d can be 0, 1 or 2 according to the length ofthe filter, where 0 is a second order filter U0, U1 is a fourthorder filter and U2 is a sixth order filter. In Fig. 2 is clearlyshown that decision criteria is 0 when the signal presentspeaks, and 1 or 2 otherwise.

Classification results are presented in Table 1 for adap-tive filter banks. A Bayes classifier with Mahalanobis dis-tance is used for brain zone classification. Selected featuresfor the classifier are maximum value and standard deviationof XL and XH . Processing time for adaptive filter banks isten times less than fixed filter banks.

5. Conclusions

Obtained results applying generalized adaptive liftingschemes allows better representation of the dynamic changeof the signal. It is represented in the classification results.Teager energy calculation as criteria for change filter ordersallows real time implementation and selectivity in the fea-ture extraction. Brain zone classification using generalizedadaptive lifting schemes presents a better performance overfixed schemes using wavelets. Analysis of the signals us-

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ing spikes or background noise reveals that it is necessaryanalyze MER signals whit out preprocessing.

6. Acknowledgments

This research is developed under the project: “Sistemaautomatizado de clasificacion de eventos fisiologicos a par-tir de patrones bioelectricos como soporte en el tratamientode la enfermedad de parkinson y otros desrdenes neu-rologicos” financed by Colciencias and Universidad Tecnol-gica de Pereira with code 1110-14-17904.

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