Machine learning-based personalized subthalamic biomarkers predict ON-OFF levodopa states in Parkinson patients

Daniel Sand, Pnina Rappel, Odeya Marmor, Atira S. Bick, David Arkadir, Bao Liang Lu, Hagai Bergman, Zvi Israel, Renana Eitan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Objective. Adaptive deep brain stimulation (aDBS) based on subthalamic nucleus (STN) electrophysiology has recently been proposed to improve clinical outcomes of DBS for Parkinson's disease (PD) patients. Many current models for aDBS are based on one or two electrophysiological features of STN activity, such as beta or gamma activity. Although these models have shown interesting results, we hypothesized that an aDBS model that includes many STN activity parameters will yield better clinical results. The objective of this study was to investigate the most appropriate STN neurophysiological biomarkers, detectable over long periods of time, that can predict OFF and ON levodopa states in PD patients. Approach. Long-term local field potentials (LFPs) were recorded from eight STNs (four PD patients) during 92 recording sessions (44 OFF and 48 ON levodopa states), over a period of 3-12 months. Electrophysiological analysis included the power of frequency bands, band power ratio and burst features. A total of 140 engineered features was extracted for 20 040 epochs (each epoch lasting 5 s). Based on these engineered features, machine learning (ML) models classified LFPs as OFF vs ON levodopa states. Main results. Beta and gamma band activity alone poorly predicts OFF vs ON levodopa states, with an accuracy of 0.66 and 0.64, respectively. Group ML analysis slightly improved prediction rates, but personalized ML analysis, based on individualized engineered electrophysiological features, were markedly better, predicting OFF vs ON levodopa states with an accuracy of 0.8 for support vector machine learning models. Significance. We showed that individual patients have unique sets of STN neurophysiological biomarkers that can be detected over long periods of time. ML models revealed that personally classified engineered features most accurately predict OFF vs ON levodopa states. Future development of aDBS for PD patients might include personalized ML algorithms.

Original languageAmerican English
Article number046058
JournalJournal of Neural Engineering
Volume18
Issue number4
DOIs
StatePublished - Aug 2021

Bibliographical note

Publisher Copyright:
© 2021 IOP Publishing Ltd.

Keywords

  • aDBS
  • burst features
  • deep learning
  • long-term LFP recording
  • personalized treatment

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