Cargando…
Parkinson’s disease tremor prediction using EEG data analysis-A preliminary and feasibility study
PURPOSE: Tremor is one of the hallmarks of Parkinson’s disease (PD) that does not respond effectively to conventional medications. In this regard, as a complementary solution, methods such as deep brain stimulation have been proposed. To apply the intervention with minimal side effects, it is necess...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668446/ https://www.ncbi.nlm.nih.gov/pubmed/38001410 http://dx.doi.org/10.1186/s12883-023-03468-0 |
_version_ | 1785149132384501760 |
---|---|
author | Farashi, Sajjad Sarihi, Abdolrahman Ramezani, Mahdi Shahidi, Siamak Mazdeh, Mehrdokht |
author_facet | Farashi, Sajjad Sarihi, Abdolrahman Ramezani, Mahdi Shahidi, Siamak Mazdeh, Mehrdokht |
author_sort | Farashi, Sajjad |
collection | PubMed |
description | PURPOSE: Tremor is one of the hallmarks of Parkinson’s disease (PD) that does not respond effectively to conventional medications. In this regard, as a complementary solution, methods such as deep brain stimulation have been proposed. To apply the intervention with minimal side effects, it is necessary to predict tremor initiation. The purpose of the current study was to propose a novel methodology for predicting resting tremors using analysis of EEG time-series. METHODS: A modified algorithm for tremor onset detection from accelerometer data was proposed. Furthermore, a machine learning methodology for predicting PD hand tremors from EEG time-series was proposed. The most discriminative features extracted from EEG data based on statistical analyses and post-hoc tests were used to train the classifier for distinguishing pre-tremor conditions. RESULTS: Statistical analyses with post-hoc tests showed that features such as form factor and statistical features were the most discriminative features. Furthermore, limited numbers of EEG channels (F3, F7, P4, CP2, FC6, and C4) and EEG bands (Delta and Gamma) were sufficient for an accurate tremor prediction based on EEG data. Based on the selected feature set, a KNN classifier obtained the best pre-tremor prediction performance with an accuracy of 73.67%. CONCLUSION: This feasibility study was the first attempt to show the predicting ability of EEG time-series for PD hand tremor prediction. Considering the limitations of this study, future research with longer data, and different brain dynamics are needed for clinical applications. |
format | Online Article Text |
id | pubmed-10668446 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106684462023-11-24 Parkinson’s disease tremor prediction using EEG data analysis-A preliminary and feasibility study Farashi, Sajjad Sarihi, Abdolrahman Ramezani, Mahdi Shahidi, Siamak Mazdeh, Mehrdokht BMC Neurol Research PURPOSE: Tremor is one of the hallmarks of Parkinson’s disease (PD) that does not respond effectively to conventional medications. In this regard, as a complementary solution, methods such as deep brain stimulation have been proposed. To apply the intervention with minimal side effects, it is necessary to predict tremor initiation. The purpose of the current study was to propose a novel methodology for predicting resting tremors using analysis of EEG time-series. METHODS: A modified algorithm for tremor onset detection from accelerometer data was proposed. Furthermore, a machine learning methodology for predicting PD hand tremors from EEG time-series was proposed. The most discriminative features extracted from EEG data based on statistical analyses and post-hoc tests were used to train the classifier for distinguishing pre-tremor conditions. RESULTS: Statistical analyses with post-hoc tests showed that features such as form factor and statistical features were the most discriminative features. Furthermore, limited numbers of EEG channels (F3, F7, P4, CP2, FC6, and C4) and EEG bands (Delta and Gamma) were sufficient for an accurate tremor prediction based on EEG data. Based on the selected feature set, a KNN classifier obtained the best pre-tremor prediction performance with an accuracy of 73.67%. CONCLUSION: This feasibility study was the first attempt to show the predicting ability of EEG time-series for PD hand tremor prediction. Considering the limitations of this study, future research with longer data, and different brain dynamics are needed for clinical applications. BioMed Central 2023-11-24 /pmc/articles/PMC10668446/ /pubmed/38001410 http://dx.doi.org/10.1186/s12883-023-03468-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Farashi, Sajjad Sarihi, Abdolrahman Ramezani, Mahdi Shahidi, Siamak Mazdeh, Mehrdokht Parkinson’s disease tremor prediction using EEG data analysis-A preliminary and feasibility study |
title | Parkinson’s disease tremor prediction using EEG data analysis-A preliminary and feasibility study |
title_full | Parkinson’s disease tremor prediction using EEG data analysis-A preliminary and feasibility study |
title_fullStr | Parkinson’s disease tremor prediction using EEG data analysis-A preliminary and feasibility study |
title_full_unstemmed | Parkinson’s disease tremor prediction using EEG data analysis-A preliminary and feasibility study |
title_short | Parkinson’s disease tremor prediction using EEG data analysis-A preliminary and feasibility study |
title_sort | parkinson’s disease tremor prediction using eeg data analysis-a preliminary and feasibility study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668446/ https://www.ncbi.nlm.nih.gov/pubmed/38001410 http://dx.doi.org/10.1186/s12883-023-03468-0 |
work_keys_str_mv | AT farashisajjad parkinsonsdiseasetremorpredictionusingeegdataanalysisapreliminaryandfeasibilitystudy AT sarihiabdolrahman parkinsonsdiseasetremorpredictionusingeegdataanalysisapreliminaryandfeasibilitystudy AT ramezanimahdi parkinsonsdiseasetremorpredictionusingeegdataanalysisapreliminaryandfeasibilitystudy AT shahidisiamak parkinsonsdiseasetremorpredictionusingeegdataanalysisapreliminaryandfeasibilitystudy AT mazdehmehrdokht parkinsonsdiseasetremorpredictionusingeegdataanalysisapreliminaryandfeasibilitystudy |