Classification and Prediction of Clinical Improvement in Deep Brain Stimulation From Intraoperative Microelectrode Recordings

被引：29
|
作者：
Kostoglou, Kyriaki [1 ]
Michmizos, Konstantinos P. [2 ]
Stathis, Pantelis [3 ]
Sakas, Damianos [3 ]
Nikita, Konstantina S. [4 ]
Mitsis, Georgios D. [5 ]
机构：
[1] McGill Univ, Dept Elect & Comp Engn, Montreal, PQ, Canada
[2] Rutgers State Univ, Dept Comp Sci, New Brunswick, NJ USA
[3] Natl & Kapodistrian Univ Athens, Dept Neurosurg, Athens, Greece
[4] Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens, Greece
[5] McGill Univ, Dept Bioengn, Montreal, PQ H3A 0C3, Canada
来源：
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2017年 / 64卷 / 05期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Deep brain stimulation (DBS); microelectrode recordings (MERs); Parkinson's Disease; random forests (RFs); subthalamic nucleus (STN); unified Parkinson's disease rating scale (UPDRS); LOCAL-FIELD POTENTIALS; SUBTHALAMIC NUCLEUS; PARKINSONS-DISEASE; NEURONAL-ACTIVITY; BASAL GANGLIA; OSCILLATORY ACTIVITY; TREMOR; SYNCHRONIZATION; ORGANIZATION; CLASSIFIERS;
D O I：
10.1109/TBME.2016.2591827
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
We present a random forest (RF) classification and regression technique to predict, intraoperatively, the unified Parkinson's disease rating scale (UPDRS) improvement after deep brain stimulation (DBS). We hypothesized that a data-informed combination of features extracted from intraoperative microelectrode recordings (MERs) can predict the motor improvement of Parkinson's disease patients undergoing DBS surgery. We modified the employed RFs to account for unbalanced datasets and multiple observations per patient, and showed, for the first time, that only five neurophysiologically interpretable MER signal features are sufficient for predicting UPDRS improvement. This finding suggests that subthalamic nucleus (STN) electrophysiological signal characteristics are strongly correlated to the extent of motor behavior improvement observed in STN-DBS.
引用
收藏
页码：1123 / 1130
页数：8
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