Deep Brain Stimulation Signal Classification using Deep Belief Networks

被引：0

作者：

Guillen-Rondon, Pablo ^{[1
]}

Robinson, Melvin D. ^{[2
]}

机构：

[1] Univ Houston, Ctr Adv Comp & Data Syst, Houston, TX 76019 USA

[2] Univ Texas Tyler, Dept Elect Engn, Tyler, TX 75799 USA

来源：

2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE & COMPUTATIONAL INTELLIGENCE (CSCI) | 2016年

关键词：

PARKINSONS-DISEASE;

D O I：

10.1109/CSCI.2016.35

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An approach to modeling complex real-world data such as biomedical signals is to develop pattern recognition techniques and robust features that capture the relevant information. In this paper, we use a deep belief network (DBN) to predict subcortical structures of patients with Parkinson's disease based on microelectrode records (MER) obtained during deep brain stimulation (DBS). We report on experiments using a data set involving 52 MER for the structures: zona incerta (Zi), subthalamic nucleus (STN), thalamus nucleus (TAL), and substantia nigra (SNR). The results show that our chosen features and network architecture produces a 99.5% accuracy of detection and classification of the subcortical structures under study. Based on the results we conclude that deep belief networks could be used to predict subcortical structure-mainly the STN for neurostimulation.

引用

页码：155 / 158

页数：4

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