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Article Dans Une Revue Engineering Applications of Artificial Intelligence Année : 2023

Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis

Résumé

This study focuses on the diagnosis of Parkinson’s Disease (PD) based on electroencephalogram (EEG) signals. A novel approach inspired by the functioning of the brain is proposed, which uses the dynamics, frequency, and temporal content of EEGs to extract new discriminant features of the disease. The generated sparse dynamic features (SDFs) allow, through a transformation, to change the point of view on the data giving access to more informative features that are more faithful to the concept of EEG generation. Nevertheless, the method remains generic and can be applied to any signal but for this application it was evaluated on a publicly available dataset containing EEG signals recorded during a 3-oddball auditory task involving subjects, of whom 25 suffer from PD. Given the adequate perspective on the data, it comes out that by using only two extracted features from the generated SDFs the healthy and unhealthy subjects are separated using a linear classifier. The classification yields an accuracy of 90.0% () using a single channel. By aggregating the information from three channels and making them vote, an accuracy of 94%, a sensitivity of 96% and a specificity of 92% is obtained. The evaluation was carried out using a nested Leave-One-Out cross-validation procedure, thus preventing data leakage problems and giving a less biased evaluation. Several tests were carried out to assess the validity and robustness of our approach.
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Dates et versions

hal-04053625 , version 1 (31-03-2023)

Identifiants

Citer

Houssem Meghnoudj, Bogdan Robu, Mazen Alamir. Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis. Engineering Applications of Artificial Intelligence, 2023, 126 (Part B), pp.106882. ⟨10.1016/j.engappai.2023.106882⟩. ⟨hal-04053625⟩
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