ETSense: Adaptive Portable Essential Tremor Monitor

描述(由申请人提供)：目标是设计、实施和临床评估ETSense，这是一种自适应、紧凑、便携式的特发性震颤(ET)监测仪，用于优化治疗干预。ET的主要特征是四肢的姿势和运动性(动作)震颤，这些震颤由各种主观震颤分级标准进行评级。这些量表都在离散的时间点上提供离散的、主观的症状评级，需要临床医生对患者进行目测评估，并且无法捕捉一天中发生的复杂波动以响应干预。在日常活动中持续客观地捕捉ET症状，并使用自适应算法对震颤类型和严重程度进行分类，将有助于临床医生更好地滴定治疗，将症状波动降至最低，并将护理扩展到农村和服务不足的人群。第一阶段的ETSense工作成功地使用了放置在ET患者手指上的传感器单元记录的运动学数据，以区分震颤和与日常活动相关的自愿运动，并客观地量化了震颤的严重程度，评分与临床医生的定性评级高度相关，为持续的ET评估提供了一个标准化的平台。震颤量化算法被外推到非标准化任务，表明在日常生活活动期间全天连续对震颤进行评级是可行的。拟议系统的三个主要创新包括：1)紧凑、便携、用户可佩戴的设备，用于在ADLS期间进行连续监测；2)智能、自适应算法，用于连续分类震颤类型和等级严重性；以及3)基于网络访问症状反应报告。临床上可部署的系统将包含在一个轻巧的手指穿戴的外壳中，供患者在家里或公共场合执行日常任务时连续佩戴。按下按钮的日记将允许患者指示何时服用药物。所有数据都将存储在内存中，以供后续分析和生成详细说明针对治疗干预措施的症状波动的报告。在第一阶段开发的自适应算法将进一步优化，以考虑到可能产生震颤、假阳性或掩盖运动性震颤信号的自愿运动。该系统将根据检测到的任何自愿运动在计分算法(即休息、动态)之间进行切换。数据收集完成后，临床医生将使用Web界面查看患者报告。这些报告将详细说明震颤的类型、严重程度和波动，以及何时采取药物来帮助临床医生优化现有的治疗干预措施或研究和开发新的治疗方案。我们假设，商用ETSense系统将1)连续量化日常生活活动期间的震颤严重程度，2)通过更好和/或更快的用药优化来改善患者结果，3)通过减少办公室就诊来降低医疗成本，以及4)通过高分辨率连续家庭监测促进对新型治疗干预措施的测试和验证。

DESCRIPTION (provided by applicant): The objective is to design, implement, and clinically assess ETSense", an adaptive, compact, portable essential tremor (ET) monitor for optimizing therapeutic interventions. ET is characterized primarily by postural and kinetic (action) tremors of the limbs, which are rated by various subjective tremor rating scales. These scales all provide a discrete, subjective symptom rating at a discrete point in time, require a clinician to visually assess the patient, and cannot capture complex fluctuations that occur throughout the day in response to interventions. Objectively capturing ET symptoms continuously during daily activities and using adaptive algorithms to both classify tremor types and severity will help clinicians better titrate therapy to minimize symptom fluctuations and expand care to rural and underserved populations. The Phase I ETSense effort successfully used kinematic data recorded from a sensor unit placed on the finger of subjects with ET to discriminate tremor from voluntary motion associated with daily activities and objectively quantified tremor severity with scores highly correlated with clinicians' qualitative ratings, providing a standardized platform for continuous ET assessment. Tremor quantification algorithms were extrapolated to non-standardized tasks, suggesting that it is feasible to rate tremor continuously throughout the day during activities of daily living. The three primary innovations of the proposed system include: 1) a compact, portable, user-worn device for continuous monitoring during ADLs, 2) intelligent, adaptive algorithms to continuously classify tremor type and rate severity, and 3) web-based access to symptom response reports. The clinically deployable system will be contained in a lightweight, finger-worn housing for continuous wear while patients perform everyday tasks at home or in public. A push button diary will allow the patient to indicate when medication is taken. All data will be stored in memory for subsequent analysis and report generation detailing symptom fluctuations in response to therapeutic interventions. Adaptive algorithms developed in Phase I will be further optimized to account for voluntary motion that can create tremor false positives or mask over kinematic tremor signals. The system will shift between scoring algorithms (i.e. rest, kinetic) based on any voluntary motion detected. After data collection is complete, clinicians will use a web-interface to view patient reports. These reports will detail tremor type, severity, and fluctuations, as well as when medication was taken to aid clinicians in optimizing existing therapeutic interventions or in the research and development of novel treatment protocols. We hypothesize that the commercial ETSense system will 1) continuously quantify tremor severity throughout the day during activities of daily living, 2) improve patient outcomes with better and/or faster medication optimization, 3) decrease healthcare costs by reducing office visits, and 4) enable the testing and validation of novel therapeutic interventions, facilitated by high-resolution continuous home monitoring.