Characterization of subcortical structures during deep brain stimulation utilizing support vector machines

Guillén, Pablo; Martínez-de-Pisón, Francisco Javier; Sánchez, Raquel Criado; Argaez, Miguel; Velázquez, Luís

doi:10.1109/iembs.2011.6091960

Cited by 18 publications

(12 citation statements)

References 10 publications

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“…To generalize our results, we performed a non-parametric test. In comparison with other studies who used support vector machines [7], decision trees [2] and K-nearest neighbors [2,6,7] we achieved similar performance with only 6 features.…”

Section: Performance Of Classifiers and Statistical Analysissupporting

confidence: 84%

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Feature selection for KNN classifier to improve accurate detection of subthalamic nucleus during deep brain stimulation surgery in Parkinson’s patients

Schiaffino

Muñoz

Villora

et al. 2017

VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016

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Section: Performance Of Classifiers and Statistical Analysissupporting

confidence: 84%

“…They did not study a reduction of feature dimension. In [2] and [6], the classification results were provided without statistical comparative analyses.…”

Section: Introduction Deep Brain Stimulation (Dbs) Is a Well-estabmentioning

confidence: 99%

Feature selection for KNN classifier to improve accurate detection of subthalamic nucleus during deep brain stimulation surgery in Parkinson’s patients

Schiaffino

Muñoz

Villora

et al. 2017

VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016

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“…Segmentation of subcortical structures in MR images is a crucial task and finds significant applications such as volumetric and morphometric analysis [2]. It is also useful for target localization in deep brain stimulation (DBS) surgery [3]. The treatment of Parkinson's disease [4] and Alzheimer's disease [5] also requires segmentation of subcortical structures.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of non rigid registration methods in atlas-based segmentation of subcortical structures

Ahmad

Ali

Khan

2012

2012 5th International Conference on BioMedical Engineering and Informatics

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In this paper we propose an atlas-based segmentation technique for subcortical structures in 3D MR images using non-rigid image registration. Further we evaluate two separate transformation models used in non-rigid registration method, namely, Thin Plate Splines (TPS) and Cubic B Splines (CBS). The optimization technique used for the registration process was Powell's method and the similarity measure used for TPS based registration was normalized mutual information whereas normalized cross correlation was used in CBS based registration algorithm. The results of automatically segmented structures (which include ventricles, caudate nucleus and putamen) obtained via atlas-subject registration were assessed against manual segmentation, using sensitivity (S), positive predictive value (P) and Dice coefficient (D) metrics. The mean ± std values of S, P and D are 0.92 ± 0.01, 0.93 ± 0.01, 0.93 ± 0.01 respectively in case of CBS whereas 0.85 ± 0.01, 0.85 ± 0.01, 0.84 ± 0.02 are the mean ± std values of S, P and D respectively in case of TPS. Thus results indicate that the better approach to segment the subcortical structures, both in terms of speed and accuracy, is by using CBS based non-rigid registration algorithm.

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“…Examples are a Linear Discriminant Classifier (LDC) or a Quadratic Discriminant Classifier (QDC) (Pinzon et al 2010). More sophisticated classifiers have also been used including Support Vector Machines (SVM) with Polynomial Kernel (Guillen et al 2011), and Hidden Markov Models (HMM) (Tahgva 2011;Orozco et al 2006).…”

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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Characterization of subcortical structures during deep brain stimulation utilizing support vector machines

Cited by 18 publications

References 10 publications

Feature selection for KNN classifier to improve accurate detection of subthalamic nucleus during deep brain stimulation surgery in Parkinson’s patients

Feature selection for KNN classifier to improve accurate detection of subthalamic nucleus during deep brain stimulation surgery in Parkinson’s patients

A comparative analysis of non rigid registration methods in atlas-based segmentation of subcortical structures

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

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