TY - GEN
T1 - Grouping Neuronal Spiking Patterns in the Subthalamic Nucleus of Parkinsonian Patients
AU - Kaku, Heet
AU - Ozturk, Musa
AU - Viswanathan, Ashwin
AU - Jimenez-Shahed, Joohi
AU - Sheth, Sameer
AU - Ince, Nuri F.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The subthalamic nucleus (STN) is a commonly used target in deep brain stimulation (DBS) to control the motor symptoms of Parkinson's Disease (PD). Identification of the spiking patterns in the STN is important in order to understand the neuropathophysiology of PD and can also assist in electrophysiological mapping of the structure. This study aims to provide a tool for grouping these firing patterns based on several extracted features from the spiking data. Single neuronal activity from the STN of PD subjects was detected and sorted to compute the binary spike trains. Several features including loca variation, bursting index and the prominence of the peak frequency of the power spectrum were extracted. Clustering of spike train segments was performed based on combination of features in 3D space to scrutinize how well they describe different firing regimes. The results show that this approach could be used to automate the grouping of stereotypic firing patterns in STN.
AB - The subthalamic nucleus (STN) is a commonly used target in deep brain stimulation (DBS) to control the motor symptoms of Parkinson's Disease (PD). Identification of the spiking patterns in the STN is important in order to understand the neuropathophysiology of PD and can also assist in electrophysiological mapping of the structure. This study aims to provide a tool for grouping these firing patterns based on several extracted features from the spiking data. Single neuronal activity from the STN of PD subjects was detected and sorted to compute the binary spike trains. Several features including loca variation, bursting index and the prominence of the peak frequency of the power spectrum were extracted. Clustering of spike train segments was performed based on combination of features in 3D space to scrutinize how well they describe different firing regimes. The results show that this approach could be used to automate the grouping of stereotypic firing patterns in STN.
UR - http://www.scopus.com/inward/record.url?scp=85077875998&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8857418
DO - 10.1109/EMBC.2019.8857418
M3 - Conference contribution
C2 - 31946800
AN - SCOPUS:85077875998
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4221
EP - 4224
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -