Identification of Spike Sources using Proximity Analysis through Hidden Markov Models

Orozco, Álvaro A.; Álvarez, Mauricio A.; Guijarro, Enrique; Castellanos, Germán

doi:10.1109/iembs.2006.4398714

Cited by 2 publications

(4 citation statements)

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“…The second most commonly investigated phase is the surgery itself, with the clinical problem frequently being the inter-operative identification of the DBS target. Every paper addressing this problem used MER analysis [10,14,21,28,33,37,39,59,63,68,70,72,76,77,78]. Instead of helping clinicians to aim for an anatomical structure, Lu et al [69] proposed a method to predict, by analyzing MER, whether or not the electrode lead is inside a clinically predefined therapeutic site of activation.…”

Section: Surgery Problemsmentioning

confidence: 99%

“…From this perspective, pre-processing can be seen as a matter of degree. For example, for machine learning methods that process MERs as input, some use a spectral representation of the entire signal [37,63], whereas others use a spectrogram [61] or Haar wavelets [10], i.e. a mixed temporal/frequential representation, and others process the temporal signal directly without explicitly representing the frequency components [14].…”

Section: The Prominent Role Of Pre-processing and Feature Engineering...mentioning

confidence: 99%

“…The majority of the papers did not employ a patient-wise validation method [10,13,14,15,16,17,18,19,20,23,24,25,30,31,28,34,35,37,38,39,40,41,42,43,44,45,46,49 [20,47], that further validation has to be done on other recording conditions or other centers [33,65,70,77,82] or that inter-patient variability was a limiting problem [46,68].…”

Section: Inter-patient Variability: the Elephant In The Roommentioning

confidence: 99%

See 2 more Smart Citations

Machine learning in deep brain stimulation: A systematic review

Peralta¹,

Jannin²,

Baxter³

2021

Artificial Intelligence in Medicine

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Section: Surgery Problemsmentioning

confidence: 99%

Section: The Prominent Role Of Pre-processing and Feature Engineering...mentioning

confidence: 99%

Section: Inter-patient Variability: the Elephant In The Roommentioning

confidence: 99%

See 1 more Smart Citation

Machine learning in deep brain stimulation: A systematic review

Peralta¹,

Jannin²,

Baxter³

2021

Artificial Intelligence in Medicine

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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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Identification of Spike Sources using Proximity Analysis through Hidden Markov Models

Cited by 2 publications

References 0 publications

Machine learning in deep brain stimulation: A systematic review

Machine learning in deep brain stimulation: A systematic review

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

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