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Shirahige L, Leimig B, Baltar A, Bezerra A, de Brito CVF, do Nascimento YSO, Gomes JC, Teo WP, Dos Santos WP, Cairrão M, Fonseca A, Monte-Silva K. Classification of Parkinson's disease motor phenotype: a machine learning approach. J Neural Transm (Vienna) 2022; 129:1447-1461. [PMID: 36335541 DOI: 10.1007/s00702-022-02552-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 10/16/2022] [Indexed: 11/08/2022]
Abstract
To assess the cortical activity in people with Parkinson's disease (PwP) with different motor phenotype (tremor-dominant-TD and postural instability and gait difficulty-PIGD) and to compare with controls. Twenty-four PwP (during OFF and ON medication) and twelve age-/sex-/handedness-matched healthy controls underwent electrophysiological assessment of spectral ratio analysis through electroencephalography (EEG) at resting state and during the hand movement. We performed a machine learning method with 35 attributes extracted from EEG. To verify the efficiency of the proposed phenotype-based EEG classification the random forest and random tree were tested (performed 30 times, using a tenfolds cross validation in Weka environment). The analyses based on phenotypes indicated a slowing down of cortical activity during OFF medication state in PwP. PD with TD phenotype presented this characteristic at resting and the individuals with PIGD presented during the hand movement. During the ON state, there is no difference between phenotypes at resting nor during the hand movement. PD phenotypes may influence spectral activity measured by EEG. Random forest machine learning provides a slightly more accurate, sensible and specific approach to distinguish different PD phenotypes. The phenotype of PD might be a clinical characteristic that could influence cortical activity.
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Affiliation(s)
- Lívia Shirahige
- Applied Neuroscience Laboratory, Department of Physical Therapy, Universidade Federal de Pernambuco, w/n Jornalista Aníbal Fernandes Avenue, Recife, PE, 50740-560, Brazil.,Post-graduation Program of Neuropsychiatry and Behavioral Sciences, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - Brenda Leimig
- Applied Neuroscience Laboratory, Department of Physical Therapy, Universidade Federal de Pernambuco, w/n Jornalista Aníbal Fernandes Avenue, Recife, PE, 50740-560, Brazil
| | - Adriana Baltar
- Applied Neuroscience Laboratory, Department of Physical Therapy, Universidade Federal de Pernambuco, w/n Jornalista Aníbal Fernandes Avenue, Recife, PE, 50740-560, Brazil.,Post-graduation Program of Neuropsychiatry and Behavioral Sciences, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - Amanda Bezerra
- Applied Neuroscience Laboratory, Department of Physical Therapy, Universidade Federal de Pernambuco, w/n Jornalista Aníbal Fernandes Avenue, Recife, PE, 50740-560, Brazil
| | | | | | - Juliana Carneiro Gomes
- Department of Biomedical Engineering, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - Wei-Peng Teo
- Physical Education and Sports Science Academic Group, National Institute of Education, Nanyang Technological University, Singapore, Singapore
| | | | - Marcelo Cairrão
- Neurodynamics Laboratory, Department of Physiology, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - André Fonseca
- Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, São Paulo, São Paulo, Brazil
| | - Kátia Monte-Silva
- Applied Neuroscience Laboratory, Department of Physical Therapy, Universidade Federal de Pernambuco, w/n Jornalista Aníbal Fernandes Avenue, Recife, PE, 50740-560, Brazil.
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