Abstract
We present a probabilistic model for classification of micro-EEG signals, recorded during deep brain stimulation surgery for Parkinson’s disease. The model uses parametric representation of neuronal background activity, estimated using normalized root-mean-square of the signal. Contrary to existing solutions using Bayes classifiers or Hidden Markov Models, our model uses smooth state-transitions represented by sigmoid functions, which ensures flexible model structure in combination with general optimizers for parameter estimation and model fitting. The presented model can easily be extended with additional parameters and constraints and is intended for fitting of a 3D anatomical model to micro-EEG data in further perspective. In an evaluation on 260 trajectories from 61 patients, the model showed classification accuracy 90.0 %, which was comparable to existing solutions. The evaluation proved the model successful in target identification and we conclude that its use for more complex tasks in the area of DBS planning and modeling is feasible.
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Notes
- 1.
- 2.
Smooth state transitions using logistic sigmoid functions lead to smooth gradient and the resulting model is therefore easier to optimize.
- 3.
Value of this normalization coefficient will however be close to one in most circumstances and reaches around 1.2 in the extreme case when \(a=b\) using sigmoid parameters from Fig. 1.
- 4.
In the case with no entry/exit depth distribution, the initial parameters were set as the middle of the trajectory for a and the 3 / 4 of the trajectory for b.
- 5.
Dimension of the parametric space searched during the evaluation phase is two, due to two optimized parameters: STN entry a and exit b, both in the range of recorded depths. The search space is further reduced by the conditions defined at the end of Model Evaluation section, especially \(a\le b\).
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Acknowledgement
The work presented in this paper has been supported by the students’ grant agency of the CTU, no. SGS16/231/OHK3/3T/13, and by the Grant Agency of the Czech republic, grant no. 16-13323S.
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Bakstein, E., Sieger, T., Novak, D., Jech, R. (2016). Probabilistic Model of Neuronal Background Activity in Deep Brain Stimulation Trajectories. In: Renda, M., Bursa, M., Holzinger, A., Khuri, S. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2016. Lecture Notes in Computer Science(), vol 9832. Springer, Cham. https://doi.org/10.1007/978-3-319-43949-5_7
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