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- Title
SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson’s disease detection.
- Authors
Tae Hoon Kim; Krichen, Moez; Ojo, Stephen; Sampedro, Gabriel Avelino; Alamro, Meznah A.
- Abstract
Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering the potential to enhance the efficiency of voice-based PD detection. This research study focuses on investigating the utilization of SS-DRPL in conjunction with deep learning algorithms for voice-based PD classification. This study encompasses a comprehensive evaluation aimed at assessing the accuracy of various predictive models, particularly deep learning methods when combined with SS-DRPL. Two deep learning architectures, namely hybrid Long Short-Term Memory and Recurrent Neural Networks (LSTM-RNN) and Deep Neural Networks (DNN), are employed and compared in terms of their ability to detect voice-based PD cases accurately. Additionally, several traditional machine learning models are also included to establish a baseline for comparison. The findings of the study reveal that the incorporation of SS-DRPL leads to improved model performance across all experimental setups. Notably, the LSTM-RNN architecture augmented with SS-DRPL achieves the highest F1-score of 0.94, indicating its superior ability to detect PD cases using voice-based data effectively. This outcome underscores the efficacy of SS-DRPL in enabling deep learning models to learn intricate patterns and correlations within the data, thereby facilitating more accurate PD classification.
- Subjects
ARTIFICIAL neural networks; MACHINE learning; PARKINSON'S disease; DEEP learning; RECURRENT neural networks; SUPERVISED learning
- Publication
Frontiers in Computational Neuroscience, 2024, p1
- ISSN
1662-5188
- Publication type
Article
- DOI
10.3389/fncom.2024.1414462