Multivariate Parkinson's Disease Prediction System

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$249,511.00
Award Year:
2007
Program:
SBIR
Phase:
Phase I
Contract:
1R43NS052020-01A2
Award Id:
85906
Agency Tracking Number:
NS052020
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
4415 EUCLID AVE, CLEVELAND, OH, 44103
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
557510625
Principal Investigator:
JOSEPHGIUFFRIDA
(216) 619-5904
JGIUFFRIDA@CLEVEMED.COM
Business Contact:
() -
hkayyali@clevemed.com
Research Institute:
n/a
Abstract
DESCRIPTION (provided by applicant): 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 earl y 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 crite ria, 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 cu rrently 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) wi ll 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 diagn ostic 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 cl inical 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 s creening 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 MP PS 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 tr ial 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.

* information listed above is at the time of submission.

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