2008 International Conference on BioMedical Engineering and Informatics
Microelectrode signals segmentation using stationary wavelet transform Cristian Guarnizo Universidad Tecnol´ogica de Pereira Electrical Engeenering Pereira, Colombia
[email protected]
Alvaro A. Orozco Universidad Tecnol´ogica de Pereira Electrical Engeenering
[email protected]
German Castellanos Universidad Nacional de Colombia Electronic Engeenering
[email protected]
Abstract
artifact and non-stationary transients, and there is a need for more objective and automated methods of microelectrode recording analysis [4, 1]. Wavelet transform is used to obtain a better representation of a signal for a multiresolution abrupt change detection. Due to wavelet decorrelation property makes possible the assumption that wavelets coefficients are independent [6] and also lineal model estimation from wavelet coefficients. Taking on count that wavelet coefficients from a particular scale represent the original signal in a specific band frequencies, so applying abrupt change detection algorithms on each scale will obtain boundaries segments from different frequency bands. The methodology here presented is compared with the one proposed in [4] showing better results on MER segmentation.
We present a methodology for the automatic segmentation of extracellular microelectrode recordings (MER) based on stationary wavelet transform and modified F test which identify segments with equal time - frequency behavior. The method was tested using synthetic signals and then applied to real MER signals, achieving artifact removal and showing a superior performance than segmentation based on time representation.
1. Introduction Parkinson’s disease is one of the most studied movement disorders. Deep brain stimulation (DBS) of some brain regions like subthalamus nucleus (STN) or Globus Pallidus internus have showed an improvement on some Parkinson’s symptoms. One crucial and difficult task to neurosurgeons is locating the target brain area to place a neurostimulator and depending on its localization, different symptoms can be disabled [2, 3]. Neurosurgeons usually listen to the static rhythm generated by a MER signal on a speaker and watch the signal waveform on an oscilloscope, and then they decide from this subjective appreciation where the microelectrode is. Also MER signal contain artifacts generated by patient movements making difficult signal recognition. The goal of microelectrode segmentation is to select a quasistationary segment that represents the characteristics of the neuronal structure it was recorded from and without containing artifacts [4, 1]. Segmentation of microelectrode data is important because standard methods for MER analysis are sensitive to
978-0-7695-3118-2/08 $25.00 © 2008 IEEE DOI 10.1109/BMEI.2008.363
2. Material and Methods Proposed segmentation method looks for abrupt changes in MER signals over stationary wavelet transform domain. In this section first we introduce a brief theory of stationary wavelet transform, and then the measure function used for abrupt change detection:
2.1. Stationary wavelet transform Stationary wavelet transform (SWT) can be obtained modifying the basic scheme of the discrete wavelet transform (DWT), each time wavelet decomposition is performed filters are upsampled, obtaining coefficients for each scale of same length as the original signal [8]. For explanation, operator ↑ 2 is introduced, this operator alternates a sequence with zeros, for example, if y =↑ 2(x) then y2i = xi
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and y2i+1 = 0. Filter G and H for each decomposition level are calculated in the following way: - g0
x -
VD = min(σs21 , σs22 )
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- g1
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Where σs21 and σs22 are variances from windows s1 and s2 , respectively. g[k] values are greater or equal to one. Abrupt changes are selected as, g[k] local maximums greater than a threshold.
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2.3. Stationary wavelet domain segmentation
Figure 1. Stationary wavelet transform for 2 decomposition levels.
Proposed methodology for abrupt change detection using wavelet transform is based on wavelet transform spatial temporal representation, where coefficients not only give temporal information but also in frequency through the different decomposition levels (subbands). Some papers shows schemes for non stationary signals abrupt change detection using wavelet transform [5], due to DWT is used, reconstruction of some bands from wavelet domain is needed for abrupt change detection (cannot detect abrupt changes because of decimation). To avoid signal reconstruction step we propose to use SWT, which allows the realization of abrupt change detection algorithms on wavelet domain without losing temporal information when the number of scales is incremented.
Gj+1 =↑ 2 (Gj ) Hj+1 =↑ 2 (Hj ) Where H0 y G0 are the originals filters used in the DWT scheme. SWT analysis is showed in figure 1. Coefficients dlk and clk from SWT have same length of original signal input, this characteristic allows abrupt change detection on wavelet different scales without losing of information in time scale.
2.3.1
2.2. Measure Function
Due to wavelet transform persistence property [3], abrupt change boundaries appear on all analysis scales. Taking on count last described wavelet property is proposed an abrupt change detection scheme averaging all measure functions obtained from each scale, so the average function has the temporal-frequency abrupt changes from all scales:
In general, abrupt change detection algorithms find changes in a time series on the parameter or parameters which define the probability density functions, estimated from two sliding windows (s1 and s2 ) over the input signal (see figure 2).
m1
Actual instant m2 -
gˆ[k] =
t
N 1 l g [k] N l=1
j j j j j j j j j j j j -
s1
Abrupt change detection averaging scales
where N is the number of decomposition levels averaged, g l [k] is the output function measure of level l and gˆ[k] is the estimated measure function, and from this last measure function we find segments boundaries selecting locals maximum over a threshold.
s2
Figure 2. Two windows scheme. Obtained results vary from windows length (m1 and m2), overlap and distance or measure function employed. Distance measure used is:
3. Validation From segmentation result it is possible to get two errors classes. Type I error occurs if a real abrupt change is not detected by segmentation procedure (false negative FN). Type II errors occurs if segmentation procedure indicates an abrupt change where there is not (false positive FP). Segmentation validation applied is precision - recall curves,
Modified F test Modified F test measure is obtained from the ratios of the variances from each window, putting in the numerator the largest value [4]: VN = max(σs21 , σs22 )
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..
g 1 [k]
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Abrupt change detection 1 Abrupt change detection 2 SWT L + 1 J JJ ^ Abrupt change detection
Figure 3. Proposed SWT abrupt change detection process
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precision and recall curves are expressed from relations of errors type I and II [7]:
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P RC =
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Figure 4. PR curves comparing segmentation performances
5. Discussion
Where PRC and RCL are precision and recall, respectively, TP is the number of correct detections. Segmentation algorithms are designed to work on different operation points. Each operation point corresponds to a pair (PRC,PRL). As the relative cost of not detected boundaries against false alarms cost depends on the application, a segmentation procedure is totally characterized by a precision over recall plot for all possible operation points. To obtain knowledge on segmentation performance measure F is introduced: F =
Eventhough the methodology here presented is a general approach, performing an analysis to each scale were found that is not necesary to apply the modified F test to all scales, just in a few scales can be extracted the boundaries. But averaging scales removes some false boundaries presented at different scales and sharpens the positive ones.
6. Conclusions
2 · P RC · RCL P RC + RCL
Wavelet transform representation and their properties (scale persistence, decorrelation) allows a better estimation of segment boundaries from a signal compound by signals from different brain regions.
Best segmentation behavior selection is based on F values, taking the largest one as a criterion.
4. Results
Segmenting real MER signals improves signal quality removing artifacts and obtaining pseudostationary segments.
The experiment was setup with a window step and length both of 0.1s. Database of 50 synthetics signals were generated in a similar way showed in [4], signals contain segments from different brain regions (thalamus, subthalamus and substantia nigra), with a frequency rate of 24 kHz. Performance of segmentation methodologies can be visualized by a PRC vs RCL plot (see figure 4), where the closest curve to point (1,1) represents the best performance. Figure 5 shows a comparison between segmentation methodology proposed in [4, 1] and wavelet segmentation for a synthetic signal, where wavelet methodology found a boundary that time domain segmentation missed. Figure 6 shows a segmentation of a real thalamus signal, where the segment selected corresponds to the largest time signal between found boundaries.
7. Acknowledgements This work was done under Colciencias contract code 1110-14-17904 project ”Sistema automatizado de clasificaci´on de eventos fisiol´ogicos a partir de patrones bioel´ectricos como soporte en el tratamiento de la enfermedad de parkinson y otros des´ordenes neurol´ogicos.
References [1] M. Aboy and H. Falkenberg. An automatic algorithm for stationary segmentation of extracellular microelectrode recordings. Medical & Biological Engineering and Computing, 44(6):511–515, 2006.
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[2] O. K. Chibirova, T. I. Aksenova, A. L. Benabid, S. Chabardes, S. Larouche, J. Rouat, and A. E. Villa. Unsupervised spike sorting of extracellular electrophysiological recording in subthalamic nucleus of parkinsonian patients. BioSystems, 79:159–171, 2005. [3] M. S. Crouse, R. D. Nowak, and R. G. Baraniuk. Waveletbased statistical signal processing using hidden markov models. IEEE Transactions on Signal Processing, 46(4):886–902, 1998. [4] J. H. Falkenberg. Segmentation of extracellular microelectrode recordings with equal power. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, Cancun, Mexico, pages 2475–2478, 17-21 September 2003. [5] E. Hitti and M.-F. Lucas. Wavelet-packet basis selection for abrupt changes detection in multicomponent signals. EUSIPCO-98, September 1998. [6] G. G. Katul, F. Ruggeri, and B. Vidakovic. Bams filtering and applications to denoising ozone concentration measurements. Journal of Statistical Planning and Inference, 136:2395–2405, 2006. [7] T. Kemp, P. Schhmidt, M. Westphal, and A. Waibel. Strategies for automatic segmentation of audio data. Proc. of the ICASSP 2000, 3(3):1423–1426, 2000. [8] G. P. Nason and B. W. Silverman. The stationary wavelet transform and some statistical applications. Notes in Statistics, 108:281–289.
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Figure 5. Segmentation of a synthetic MER signal (2s STN, 2 s T, 1 s STN, 3 s T). (a) Sintetic signal. (b) Wavelet segmentation with N=10. (c) Time domain segmentation.
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Figure 6. Segmentation for a real thalamus signal. (a) Complete register and segment selected (gray). (b) gˆ[k] with N = 10.
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