Non-Invasive Machine Learned Device to Personalize Arrhythmia Therapy

项目摘要 心律失常是一个非常常见的原因的症状，休假，住院， 程序和医疗费用。心电图监测设备的出现有助于管理，包括 可穿戴设备和智能手机。然而，虽然这些ECG设备检测心律失常，但它们提供有限的 信息，以告知药物和侵入性消融治疗之间的治疗决策。值得注意的是，目前 器械忽略了心律失常空间模式的关键信息，以及心律失常是发生在左侧还是右侧 心脏，如果可用的话，可以用来为每个病人个性化的管理决策。 该项目开发了一种非侵入性的基于AI的躯干映射设备， 通过在频率、空间模式和位置方面充分表征心律失常的动态监测 包括左心房或右心房。该工具将是一个可穿戴设备，提供一流的心律失常 “电影”的心脏，但很简单，可以应用于病人在家里，而不需要在 医院计算机断层扫描（CT）或磁共振（MR）成像。执行计算 并传输给护理人员，使他们能够决定是否将患者直接转介给 侵入性消融或开始药物治疗。这种方法有可能大大改善临床护理。 该项目建立在已发布的新型躯干映射技术和机器学习方法的基础上 通过PI和Co-Is标测心律失常，无需使用57个体表电极导线进行CT或MR成像， 比现有的技术。Aim 1将开发机器学习和基于向量的方法， 从躯干识别心律失常位置，并将其准确性与机器学习和专家进行比较 传统的ECG分析。目标2将确定最小的躯干电极导线配置和定位部位 并表征心律失常。这构成了我们计划的第二阶段应用程序的基础， 可穿戴贴片作为基于机器的新型流动管理系统的一部分。 这项研究在多个层面产生了影响。科学上，我们建立了新的矢量和机器 学习策略，以在非侵入性平台上表征简单（非诱发性）心律失常。 未来的项目将扩展到其他心律失常。临床上，心律失常治疗的个性化， 完全远程的可穿戴设备可以中断当前的顺序护理和资源利用， 为偏远和服务不足地区的患者提供治疗。从商业角度来看，这种方法 可以很容易地向医疗机构、医生、战略合作伙伴和患者货币化。我们 团队在本提案的科学、临床、监管和业务方面经验丰富。

Project Summary Cardiac arrhythmias are a very common cause of symptoms, days off work, hospitalization, procedures and healthcare costs. ECG monitoring devices have emerged to help management, including wearables and smart phones. However, while these ECG devices detect arrhythmias, they give limited information to inform treatment decisions between drug and invasive ablation therapy. Notably, current devices omit critical information on spatial patterns of arrhythmias and whether they arise in left or right heart that, if available, could be used to personalize management decisions for each patient. The project develops a non-invasive AI-based torso mapping device that extends any available ambulatory monitor by fully characterizing arrhythmias in terms of rate, spatial pattern and location including left or right atrium. The tool will be a wearable device that provides first-in-class arrhythmia ‘movies’ in the heart, yet is simple enough to be applied by patients at home without the need for in hospital computed tomography (CT) or magnetic resonance (MR) imaging. Computations are performed in the cloud and transmitted to caregivers, enabling them to decide whether to refer a patient directly for invasive ablation or start a medication. This approach has the potential to greatly improve clinical care. The project builds on novel torso mapping technology and machine learning methods published by the PI and Co-Is to map arrhythmias without CT or MR imaging using 57 body surface leads, smaller than existing technologies. Aim 1 will develop machine learning and vectorially-based approaches to identify arrhythmia location from the torso, and compare its accuracy to machine learning and expert analysis of traditional ECGs. Aim 2 will identify the smallest torso lead configuration and site to localize and characterize arrhythmias. This forms the basis for our planned phase II application to build a wearable patch as part of a machine-based novel ambulatory management system. This study delivers impact at multiple levels. Scientifically, we build novel vectorial and machine learning strategies to characterize simple (non-fibrillatory) arrhythmias on a non-invasive platform. Future projects will extend to other arrhythmias. Clinically, the personalization of arrhythmia therapy by a fully remote wearable device can disrupt current sequential care and resource utilization, and improve outcomes for patients in remote and under-served areas. From a business perspective, this approach can be readily monetized to healthcare organizations, physicians, strategic partners and patients. Our team is experienced in the science, clinical, regulatory and business aspects of this proposal.