Enhanced LSTM by Attention Mechanism for Early Detection of Parkinson's Disease through Voice Signals
Arman Mohammadigilani, Hani Attar, Hamidreza Ehsani Chimeh, Mostafa Karami
TL;DR
The paper tackles predicting UPDRS scores from voice signals in early Parkinson's disease. It introduces an enhanced LSTM model with an attention mechanism, augmented by jittering-based data augmentation and Recursive Feature Elimination for feature selection. Using the UC Irvine PD voice dataset, evaluated through five-fold cross-validation, the approach achieves lower $MSE$ and higher $R^2$ than baselines, indicating improved predictive accuracy. While promising for timely, individualized PD assessment, the study underscores the need for broader clinical validation and generalization to diverse cohorts.
Abstract
Parkinson's disease (PD) is a neurodegenerative condition characterized by notable motor and non-motor manifestations. The assessment tool known as the Unified Parkinson's Disease Rating Scale (UPDRS) plays a crucial role in evaluating the extent of symptomatology associated with Parkinson's Disease (PD). This research presents a complete approach for predicting UPDRS scores using sophisticated Long Short-Term Memory (LSTM) networks that are improved using attention mechanisms, data augmentation techniques, and robust feature selection. The data utilized in this work was obtained from the UC Irvine Machine Learning repository. It encompasses a range of speech metrics collected from patients in the early stages of Parkinson's disease. Recursive Feature Elimination (RFE) was utilized to achieve efficient feature selection, while the application of jittering enhanced the dataset. The Long Short-Term Memory (LSTM) network was carefully crafted to capture temporal fluctuations within the dataset effectively. Additionally, it was enhanced by integrating an attention mechanism, which enhances the network's ability to recognize sequence importance. The methodology that has been described presents a potentially practical approach for conducting a more precise and individualized analysis of medical data related to Parkinson's disease.