A Hybrid U-Lossian Deep Learning Network for Screening and Evaluating Parkinson’s Disease

Abstract

Speech impairment analysis and processing technologies have evolved substantially in recent years, and the use of voice as a biomarker has gained popularity. We have developed an approach for clinical speech signal processing to demonstrate the promise of deep learning-driven voice analysis as a screening tool for Parkinson’s Disease (PD), the world’s second most prevalent neurodegenerative disease. Detecting Parkinson’s disease symptoms typically involves an evaluation by a movement disorder expert, which can be difficult to get and yield varied findings. A vocal digital biomarker might supplement the time-consuming traditional manual examination by recognizing and evaluating symptoms that characterize voice quality and level of deterioration. We present a deep learning based, custom U-lossian model for PD assessment and recognition. The study’s goal was to discover anomalies in the PD-affected voice and develop an automated screening method that can discriminate between the voices of PD patients and healthy volunteers while also providing a voice quality score. The classification accuracy was evaluated on two speech corpora (Italian PVS and own Lithuanian PD voice dataset) and we have found the result to be medically appropriate, with values of 0.8964 and 0.7949, confirming the proposed model’s high generalizability.