Detection of Parkinson's disease using Breathing Signals

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects millions of people worldwide. The disease primarily impacts the dopaminergic neurons in the substantia nigra, leading to motor symptoms such as tremors, muscle rigidity, and loss of balance. Early diagnosis of PD is crucial for better management of the disease, allowing for early initiation of treatment and improved patient outcomes. Currently, there is no definitive test for PD; diagnosis relies primarily on the evaluation of clinical symptoms, which often appear several years after the onset of the disease. This delayed detection limits the potential for early intervention and disease management. As a result, there is a growing need for non-invasive, cost-effective, and reliable methods to detect PD at an early stage. In this study, I propose a novel approach for early PD detection by capturing respiratory breathing patterns during sleep using smartphone-generated ultrasonic rays. I employ a Deep Neural Network (DNN) model combining a convolutional neural network (CNN) and a recurrent neural network (RNN) for the classification of the breathing signals into two classes: PD and control.