PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network

Parkinson’s disease (PD) is a progressive degenerative brain disease that worsens with age, causing areas of the brain to weaken. Vocal dysfunction often emerges as one of the earliest and most prominent indicators of Parkinson’s disease, with a significant number of patients exhibiting vocal impairments during the initial stages of the illness. In view of this, to facilitate the diagnosis of Parkinson’s disease through the analysis of these vocal characteristics, this study focuses on exerting a combination of mel spectrogram and MFCC as spectral features. This study adopts Italian raw audio data to establish an efficient detection framework specifically designed to classify the vocal data into two distinct categories: healthy individuals and patients diagnosed with Parkinson’s disease. To this end, the study proposes a hybrid model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the detection of Parkinson’s disease. Certainly, CNNs are employed to extract spatial features from the extracted spectro-temporal characteristics of vocal data, while LSTMs capture temporal dependencies, accelerating a comprehensive analysis of the development of vocal patterns over time. Additionally, the merging of a multi-head attention mechanism significantly enhances the model’s ability to concentrate on essential details, hence improving its overall performance. This unified method aims to enhance the detection of subtle vocal changes associated with Parkinson’s, enhancing overall diagnostic accuracy. The findings declare that this model achieves a noteworthy accuracy of 99.00% for the Parkinson’s disease detection process.