Advanced Methods for Target Navigation using Microelectrode Recordings in Stereotactic Neurosurgery for Deep Brain Stimulation

Gemmar, Peter; Gronz, Oliver; Henrichs, T.; Hutter, Frank

doi:10.1109/cbms.2008.54

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…Another common approach is the time-frequency analysis, which consists in transforming the MER signals to different mathematical representation spaces. Examples include the Short-Time Fourier Transform space (STFT) for power spectrum analysis (Chuang et al 2012;Novak et al 2007), the Wavelet Transform space (WT) (Gemmar et al 2008), and the Hilbert-Huang Transform space (HHT) (Pinzon et al 2009). Within the wavelet space, analysis by adaptive filter banks or adaptive wavelets (AW) is one of the most powerful methods for feature extraction in MER signals (Giraldo et al 2008;Pinzon et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Multi-task learning for subthalamic nucleus identification in deep brain stimulation

Cardona

López

Orozco

2017

Int. J. Mach. Learn. & Cyber.

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Deep brain stimulation (DBS) of Subthalamic nucleus (STN) is the most successful treatment for advanced Parkinson's disease. Localization of the STN through Microelectrode recordings (MER) is a key step during the surgery. However, it is a complex task even for a skilled neurosurgeon. Different researchers have developed methodologies for processing and classification of MER signals to locate the STN. Previous works employ the classical paradigm of supervised classification, assuming independence between patients. The aim of this paper is to introduce a patientdependent learning scenario, where the predictive ability for STN identification at the level of a particular patient, can be used to improve the accuracy for STN identification in other patients. Our inspiration is the multi-task learning framework, that has been receiving increasing interest within the machine learning community in the last few years. To this end, we employ the multi-task Gaussian processes framework that exhibits state of the art performance in multi-task learning problems. In our context, we assume that each patient undergoing DBS is a different task, and we refer to the method as multi-patient learning. We show that the multipatient learning framework improves the accuracy in the identification of STN in a range from 4.1% to 7.7%, compared to the usual patient-independent setup, for two different datasets. Given that MER are non stationary and noisy signals. Traditional approaches in machine learning fail to recognize accurately the STN during DBS. By contrast in our proposed method, we properly exploit correlations between patients with similar diseases, obtaining an additional information. This information allows to improve the accuracy not only for locating STN for DBS but also for other biomedical signal classification problems.

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Section: Introductionmentioning

confidence: 99%

Multi-task learning for subthalamic nucleus identification in deep brain stimulation

Cardona

López

Orozco

2017

Int. J. Mach. Learn. & Cyber.

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“…Otro enfoque muy común es el análisis tiempo-frecuencia, en el cual las señales MER se transforman a distintos espacios, por ejemplo, el espacio de la transformada corta de Fourier (STFT) (Novak, Daniluk, Elias, & Nazzaro, 2007), el espacio wavelet (WT) (Gemmar, Gronz, Henrichsand, & Hertel, 2008) y también con métodos empíri-cos como la transformada Hilbert-Huang (HHT) (Pinzon, Garcés, Orozco, & Nazzaro, 2009). Estas metodologías entregan resultados aceptables de clasificación, sin embargo no ofrecen buena generalización al momento de validar un sistema de identificación automática.…”

Section: Introductionunclassified

Representación óptima de señales MER aplicada a la identificación de estructuras cerebrales durante la estimulación cerebral profunda

Cardona

Álvarez

Gutiérrez

2015

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InvestIgacIón ResumenLa 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. Palabras clave: aprendizaje de máquina, enfermedad de Parkinson, estimulación cerebral profunda, procesamiento digital de señales, señales MER. AbstractIdentification 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 Representación óptima de señales MER aplicada a la identificación de estructuras cerebrales durante la estimulación cerebral profunda

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“…Finally, the last approach is a combination called time-frequency analysis. In these, MER signals are transformed into a different space, i.e, wavelet space [3], Short-time Fourier Transform (STFT) space [4], Gaussian mixture models (GMM) space, and others [5]- [7]. As result, hidden information can be revealed and used for nuclei discrimination.…”

Section: Introductionmentioning

confidence: 99%

Towards high accuracy classification of MER signals for target localization in Parkinson's disease

Pinzon-Morales

Gutiérrez

Carmona-Villada

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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In recent years Microelectrode recording (MER) analysis has proved to be a powerful localization tool of basal ganglia for Parkinson disease's treatment, especially the Subthalamic Nucleus (STN). In this paper, a signal-dependent method is presented for identification of the STN and other brain zones in Parkinsonian patients. The proposed method, refereed as optimal wavelet feature extraction method (OWFE), is constructed by lifting schemes (LS), which are a flexible and fast implementation of the wavelet transform (WT). The operators in the LS are optimized by means of Genetic Algorithms and Lagrange multipliers considering information contained in MER signals. Then a basic Bayesian classifier (LDC) is used to identify STN and other types of basal ganglia nuclei. The proposed method introduced several advantages from similar works reported in literature. First, the method is signal-dependent and non a priori information is required to decompose the MER signal. Second, the classification accuracy is mostly depended on the feature selection stage because it is not enhanced by elaborated classifiers such as support vector machines or hidden Markov models. Finally, the generalization property of the OWFE has been validated with two databases and different types of classifiers such as k-NN classifier and quadratic Bayesian classifier (QDC). Results have shown that proposed method is able to identify the STN with average accuracy superior than 97%.

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Advanced Methods for Target Navigation using Microelectrode Recordings in Stereotactic Neurosurgery for Deep Brain Stimulation

Cited by 12 publications

References 7 publications

Multi-task learning for subthalamic nucleus identification in deep brain stimulation

Multi-task learning for subthalamic nucleus identification in deep brain stimulation

Representación óptima de señales MER aplicada a la identificación de estructuras cerebrales durante la estimulación cerebral profunda

Towards high accuracy classification of MER signals for target localization in Parkinson's disease

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