Optimal Representation of MER Signals Applied to the Identification of Brain Structures During Deep Brain Stimulation
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 i...
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| Formato: | Artículo (Article) |
| Lenguaje: | Español (Spanish) |
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Universidad Distrital Francisco José de Caldas. Colombia
2015
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| Acceso en línea: | http://hdl.handle.net/11349/20830 |
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ir-11349-20830 |
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Universidad Distrital Francisco José de Caldas |
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| language |
Español (Spanish) |
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deep brain stimulation digital signal processing machine learning MER signals Parkinson’s disease aprendizaje de máquina enfermedad de Parkinson estimulación cerebral profunda procesamiento digital de señales señales MER |
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deep brain stimulation digital signal processing machine learning MER signals Parkinson’s disease aprendizaje de máquina enfermedad de Parkinson estimulación cerebral profunda procesamiento digital de señales señales MER Vargas Cardona, Hernán Darío Álvarez López, Mauricio Orozco Gutiérrez, Álvaro Optimal Representation of MER Signals Applied to the Identification of Brain Structures During Deep Brain Stimulation |
| description |
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. 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 validate 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 identification 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. |
| format |
Artículo (Article) |
| author |
Vargas Cardona, Hernán Darío Álvarez López, Mauricio Orozco Gutiérrez, Álvaro |
| author_facet |
Vargas Cardona, Hernán Darío Álvarez López, Mauricio Orozco Gutiérrez, Álvaro |
| author_sort |
Vargas Cardona, Hernán Darío |
| title |
Optimal Representation of MER Signals Applied to the Identification of Brain Structures During Deep Brain Stimulation |
| title_short |
Optimal Representation of MER Signals Applied to the Identification of Brain Structures During Deep Brain Stimulation |
| title_full |
Optimal Representation of MER Signals Applied to the Identification of Brain Structures During Deep Brain Stimulation |
| title_fullStr |
Optimal Representation of MER Signals Applied to the Identification of Brain Structures During Deep Brain Stimulation |
| title_full_unstemmed |
Optimal Representation of MER Signals Applied to the Identification of Brain Structures During Deep Brain Stimulation |
| title_sort |
optimal representation of mer signals applied to the identification of brain structures during deep brain stimulation |
| publisher |
Universidad Distrital Francisco José de Caldas. Colombia |
| publishDate |
2015 |
| url |
http://hdl.handle.net/11349/20830 |
| _version_ |
1712444497042866176 |
| spelling |
ir-11349-208302019-09-19T21:44:46Z Optimal Representation of MER Signals Applied to the Identification of Brain Structures During Deep Brain Stimulation Representación óptima de señales MER aplicada a la identificación de estructuras cerebrales durante la estimulación cerebral profunda Vargas Cardona, Hernán Darío Álvarez López, Mauricio Orozco Gutiérrez, Álvaro deep brain stimulation digital signal processing machine learning MER signals Parkinson’s disease aprendizaje de máquina enfermedad de Parkinson estimulación cerebral profunda procesamiento digital de señales señales MER 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. 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 validate 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 identification 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. La identificación de señales cerebrales provenientes de microelectrodos de registro (MER) es un procedimiento clave en la estimulación cerebral profunda (DBS en inglés) aplicada en pacientes con enfermedad de Parkinson (EP). El propósito de esta investigación es identificar con alta precisión una estructura cerebral llamada núcleo subtalámico (STN), ya que es la estructura objetivo donde se logran los mejores resultados terapéuticos de la DBS. Para ello, se presenta un enfoque de representación óptima de señales MER mediante el método de Frames (MOF por sus siglas en inglés), con el cual se obtienen coeficientes que minimizan la norma Euclidiana de orden 2. A partir de los coeficientes óptimos se realiza una extracción de características de las señales combinando diccionarios wavelet packet y coseno. Para tener un marco de comparación con el estado del arte, también se caracterizan las señales utilizando la transformada wavelet discreta (DWT) con diferentes funciones madre. La metodología propuesta se valida en una base de datos real, y se emplean máquinas de aprendizaje supervisadas simples, como el clasificador K-Nearest Neighbors (K-NN), el clasificador lineal bayesiano (LDC) y el cuadrático (QDC). Los resultados de clasificación que se obtienen con el método propuesto mejoran significativamente el rendimiento alcanzado con la DWT, de manera que se logra una identificación positiva del STN superior al 97,6%. Los índices de identificación logrados por el MOF son muy precisos, ya que potencialmente se puede obtener una tasa de falsos positivos menores al 2% durante la DBS. 2015-07-01 2019-09-19T21:44:46Z 2019-09-19T21:44:46Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/9011 10.14483/udistrital.jour.tecnura.2015.3.a01 http://hdl.handle.net/11349/20830 spa https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/9011/10369 https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/9011/10967 Derechos de autor 2015 Revista Tecnura application/pdf text/html Universidad Distrital Francisco José de Caldas. Colombia Tecnura Journal; Vol 19 No 45 (2015): July - September; 15-28 Tecnura; Vol. 19 Núm. 45 (2015): Julio - Septiembre; 15-28 2248-7638 0123-921X |
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12,131701 |