Resumo

VARGAS CARDONA, Hernán Darío; ALVAREZ, Mauricio A.  e  OROZCO GUTIERREZ, Álvaro. Optimal Representation of MER Signals Applied to the Identification of Brain StructuresDuring Deep Brain Stimulation. Tecnura [online]. 2015, vol.19, n.45, pp.15-27. ISSN 0123-921X.  https://doi.org/10.14483/udistrital.jour.tecnura.2015.3.a01.

Identification of brain signals from microelectrode recordings (MER) is a key procedure during deep brain stimulation (DBS) applied in Parkinson's disease patients. The main purpose of this research work is to identify with high accuracy a brain structure called subthalamic nucleus (STN), since it is the target structure where the DBS achieves the best therapeutic results. To do this, we present an approach for optimal representation of MER signals through method of frames (MOF). We obtain coefficients that minimize the Euclidean norm of order two. From optimal coefficients, we extract some features from signals combining the wavelet packet and cosine dictionaries. For a comparison frame with the state of the art, we also process the signals using the discrete wavelet transform (DWT) with several mother functions. We val idate the proposed methodology in a real data base. We employ simple supervised machine learning algorithms, such as the K-Nearest Neighbors classifier (K-NN), a linear Bayesian classifier (LDC) and a quadratic Bayesian classifier (QDC). Classification results obtained with the proposed method improve significantly the performance of the DWT. We achieve a positive identificaron of the STN superior to 97,6%. Identification outcomes achieved by the MOF are highly accurate, as we can potentially get a false positive rate of less than 2% during the DBS.

Palavras-chave : deep brain stimulation; digital signal processing; machine learning; MER signals; Parkinson's disease.

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