The objective is to develop a multivariate system capable of recording, quantifying, analyzing, and presenting multiple symptoms related to the diagnosis of Parkinson's disease (PD). The clinical system will aid in early screening by objectively quantifying several key PD symptoms including motor features, sense of smell, sleep parameters, voice modulation, and other relevant neurological signs. The system will aid in PD screening and diagnosis, defining diagnostic criteria, and quantifying treatment protocol effects. The objective is to develop a multivariate system capable of recording, quantifying, analyzing, and presenting multiple symptoms related to the diagnosis of Parkinson's disease (PD). No biological markers currently exist for antemortem PD diagnosis. Diagnosis relies upon presence and progression of clinical features and confirmation on neuropathology. Clinicopathologic studies have shown significant false- positive and false-negative diagnosis rates. Several clinical features have been correlated with early PD including motor function, olfaction, heart rate variability and electromyography during sleep, voice modulation, and oculomotor activity. The proposed Multivariate Parkinson's Prediction System (MPPS) will be a non-invasive, easy to use system of small, lightweight, wirelessly networked modules to quantify multiple PD symptoms. Modules will include motor, physiological, speech, and olfaction. The MPPS will aid in PD screening and diagnosis, defining diagnostic criteria, and quantifying treatment protocol effects. The system should allow general practitioners to screen for PD. The MPPS will consist of small, lightweight, telemetry hardware modules and a clinical base station. A motor module will sense three-dimensional motion. A physiologic module will capture standard electrophysiology inputs. A voice module will utilize a wireless microphone to capture quantitative speech features. An olfaction module will integrate a reliable, off the shelf system. The clinical base station will consist of a small, lightweight laptop computer with an integrated radio and clinical interface software. The base station will detect area modules, process data, and report clinical details. The clinical system will aid in early screening by objectively quantifying several key PD symptoms including motor features, olfaction, sleep parameters, voice modulation, and other relevant neurological signs. A patient database will link clinical groups to guide diagnostic criteria and track symptom progression. It will maximize patient safety and comfort through a small, non-invasive, unobtrusive, untethered design that can be used in the clinic or home. It will illustrate through large, well-designed, multi-center clinical trials that the MPPS accurately captures PD symptoms and differentiates between PD and non-PD subjects. Specifically, for Phase I we will integrate prototype hardware, design algorithms for clinical feature extraction, develop a software interface, and conduct a clinical trial with PD and non-PD subjects. We hypothesize that we can accurately record data, extract objective clinical features, and accurately predict between PD and non-PD subjects using multiple objective clinical measures as inputs. The clinical utility of the final Phase I prototype device will also be evaluated by several movement disorder experts. ? ? ? ?
