Detection of the subthalamic nucleus in microelectrographic recordings in Parkinson disease using the high-frequency (&gt; 500 Hz) neuronal background

Novák, Péter; Daniluk, Slawomir; Ellias, Samuel; Nazzaro, Jules M.

doi:10.3171/jns.2007.106.1.175

Cited by 57 publications

(56 citation statements)

References 19 publications

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“…Therefore, we choose the corresponding threshold TH 1 below the mean value for AD EEG and above the mean value for control EEG. With this choice of thresholds TH 1, TH 2 and TH 3 , the averaged features f 1 , f 2 , and f 3 are then mapped into music and visual display, where music notes and graphical objects are triggered by above-or below-threshold values of relative power respectively.…”

Section: Th1 Th2mentioning

confidence: 99%

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From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

Elgendi

Dauwels

Rebsamen

et al. 2014

Neural Computation, Neural Devices, and Neural Prosthesis

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Abstract. In neurofeedback, brain waves are transformed into sounds or music, graphics, and other representations, to provide real-time information on ongoing waves and patterns in the brain. Here we present various forms of neurofeedback, including sonification, sonification in combination with visualization, and at last, immersive neurofeedback, where auditory and visual feedback is provided in a multi-sided immersive environment in which participants are completely surrounded by virtual imagery and 3D sound. Neural feedback may potentially improve the user's (or patient's) ability to control brain activity, the diagnosis of medical conditions, and the rehabilitation of neurological or psychiatric disorders. Several psychological and medical studies have confirmed that virtual immersive activity is enjoyable, stimulating, and can have a healing effect. As an illustration, neurofeedback is generated from electroencephalograms (EEG) of Alzheimer's disease (AD) patients and healthy subjects. The auditory, visual, and immersive representations of Alzheimer's EEG differ substantially from healthy EEG, potentially yielding novel diagnostic tools. Moreover, such alternative representations of AD EEG are natural and intuitive, and hence easily accessible to laymen (AD patients and family members), and can provide insight into the abnormal brainwaves associated with AD.

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Section: Th1 Th2mentioning

confidence: 99%

“…This large number of neurons conduct electrical signals-action potentials-measured in microvolt which can be amplified and heard via speakers [2]. Neurophysiologists often listen and/or visualize neuronal electromagnetically-generated communications that are produced by individual neurons [3].…”

Section: Introductionmentioning

confidence: 99%

From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

Elgendi

Dauwels

Rebsamen

et al. 2014

Neural Computation, Neural Devices, and Neural Prosthesis

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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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“…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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Detection of the subthalamic nucleus in microelectrographic recordings in Parkinson disease using the high-frequency (> 500 Hz) neuronal background

Cited by 57 publications

References 19 publications

From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

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

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