Multivariate Parkinson's Disease Prediction System

描述（由申请人提供）：目的是开发能够记录、量化、分析和呈现与帕金森病（PD）诊断相关的多种症状的多变量系统。该临床系统将通过客观量化几种关键PD症状（包括运动特征、嗅觉、睡眠参数、声音调制和其他相关神经学体征）来帮助早期筛查。该系统将有助于PD筛查和诊断，定义诊断标准，并量化治疗方案效果。目的是开发一个多变量系统，能够记录，量化，分析和提出与帕金森病（PD）诊断相关的多种症状。目前尚无生物学标记物可用于死前PD诊断。诊断依赖于临床特征的存在和进展以及神经病理学的确认。临床病理学研究显示了显著的假阳性和假阴性诊断率。一些临床特征与早期PD相关，包括运动功能、嗅觉、心率变异性和睡眠期间的肌电图、声音调制和眼活动。所提出的多变量帕金森氏预测系统（MPPS）将是一个非侵入性的，易于使用的系统，小，重量轻，无线网络模块，以量化多种PD症状。模块将包括运动，生理，语音和嗅觉。MPPS将有助于PD筛查和诊断、定义诊断标准和量化治疗方案效果。该系统应允许全科医生筛选PD。MPPS将由小型、轻型遥测硬件模块和临床基站组成。一个运动模块将感知三维运动。生理模块将捕获标准电生理学输入。语音模块将利用无线麦克风来捕获定量语音特征。嗅觉模块将集成一个可靠的现成系统。临床基站将由一台小型、轻便的笔记本电脑组成，带有集成的无线电和临床接口软件。基站将检测区域模块、处理数据并报告临床细节。临床系统将通过客观量化几种关键PD症状（包括运动特征、嗅觉、睡眠参数、声音调制和其他相关神经学体征）来帮助早期筛查。患者数据库将链接临床组，以指导诊断标准并跟踪症状进展。它将最大限度地提高患者的安全性和舒适性，通过一个小的，非侵入性的，不显眼的，不受束缚的设计，可用于诊所或家庭。它将通过大型、精心设计的多中心临床试验来说明MPPS准确地捕获PD症状并区分PD和非PD受试者。具体而言，对于第一阶段，我们将整合原型硬件，设计临床特征提取算法，开发软件界面，并对PD和非PD受试者进行临床试验。我们假设，我们可以准确地记录数据，提取客观的临床特征，并使用多个客观的临床指标作为输入，准确地预测PD和非PD受试者之间的关系。最终I期原型器械的临床效用也将由几位运动障碍专家进行评估。

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 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.