MersV1, Main, Exploration, bibRecord, 000628

Detection of Subthalamic Nucleus using Time-Frequency Features of Microelectrode recordings and Random Forest Classifier.

Identifieur interne : 000628 ( Main/Exploration ); précédent : 000627; suivant : 000629

Detection of Subthalamic Nucleus using Time-Frequency Features of Microelectrode recordings and Random Forest Classifier.

Auteurs : P A Karthick ; Kai Rui Wan ; R. Yuvaraj ; Angela Aq See ; Nicolas Kon Kam King ; Justin Dauwels

Source :

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference [ 1557-170X ] ; 2019.

RBID : pubmed:31946787

Abstract

Accurate localization of subthalamic nucleus (STN) is a key prior in deep brain stimulation (DBS) surgery for the patients with advanced Parkinson's disease (PD). Microelectrode recordings (MERs) along with preplanned trajectories are often employed for the STN localization and it remains challenging task. These MER signals are nonstationary and multicomponent in nature. In this study, we propose a system based on time-frequency features of MERs to differentiate the STN and non-STN regions. We assessed the system with 50 MER trajectories from 26 PD patients who have undergone DBS surgery. The signals are pre-processed and subjected to six-level wavelet decomposition. Then, the entropy is computed from the detailed and approximate coefficients. These features are fed to the random forest classifier and the model is evaluated by leave one patient out cross-validation. The results show that entropy associated with detailed wavelet coefficients (D₁and D₂) are higher in STN where as it is lower in other wavelet scales. All extracted features except entropy from approximate coefficients are found to have significant difference between non-STN and STN (p<; 0.05). The random forest classifier achieves about 83% accuracy and 87% precision in differentiating the STN and non-STN regions.

DOI: 10.1109/EMBC.2019.8857080
PubMed: 31946787

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en">Accurate localization of subthalamic nucleus (STN) is a key prior in deep brain stimulation (DBS) surgery for the patients with advanced Parkinson's disease (PD). Microelectrode recordings (MERs) along with preplanned trajectories are often employed for the STN localization and it remains challenging task. These MER signals are nonstationary and multicomponent in nature. In this study, we propose a system based on time-frequency features of MERs to differentiate the STN and non-STN regions. We assessed the system with 50 MER trajectories from 26 PD patients who have undergone DBS surgery. The signals are pre-processed and subjected to six-level wavelet decomposition. Then, the entropy is computed from the detailed and approximate coefficients. These features are fed to the random forest classifier and the model is evaluated by leave one patient out cross-validation. The results show that entropy associated with detailed wavelet coefficients (D<sub>1</sub>
and D<sub>2</sub>
) are higher in STN where as it is lower in other wavelet scales. All extracted features except entropy from approximate coefficients are found to have significant difference between non-STN and STN (p<; 0.05). The random forest classifier achieves about 83% accuracy and 87% precision in differentiating the STN and non-STN regions.</div>
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Detection of Subthalamic Nucleus using Time-Frequency Features of Microelectrode recordings and Random Forest Classifier.

Detection of Subthalamic Nucleus using Time-Frequency Features of Microelectrode recordings and Random Forest Classifier.

Source :

Abstract

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