Non-Invasive Machine Learned Device to Personalize Arrhythmia Therapy

项目摘要 心律失常是一种非常常见的症状，如休班、住院、 程序和医疗费用。心电监测设备已经出现，以帮助管理，包括 可穿戴设备和智能手机。然而，尽管这些心电设备检测到心律失常，但它们提供的信息有限 为药物和侵入性消融治疗之间的治疗决策提供信息。值得注意的是，当前 设备省略了有关心律失常的空间模式以及心律失常是发生在左侧还是右侧的关键信息 心脏，如果有的话，可以用来为每个患者制定个性化的管理决策。 该项目开发了一种基于人工智能的非侵入性躯干映射设备，它可以扩展任何可用的 在心率、空间模式和位置方面全面表征心律失常的动态监测 包括左房或右房。该工具将是一种可穿戴设备，可提供一流的心律失常 电影在心中，但足够简单，患者可以在家里使用，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直接转介患者进行治疗 侵入性消融或开始药物治疗。这种方法有可能极大地改善临床护理。 该项目建立在发表的新躯干映射技术和机器学习方法的基础上 由PI和Co-IS使用57个体表导联，在没有CT或MR成像的情况下映射心律失常，较小 而不是现有技术。目标1将开发机器学习和基于矢量的方法来 从躯干识别心律失常的位置，并将其准确性与机器学习和专家进行比较 对传统心电图机的分析。目标2将确定要本地化的最小躯干引线配置和位置 并描述心律失常的特征。这构成了我们计划的第二阶段应用程序的基础，以构建 可穿戴贴片作为基于机器的新型非卧床管理系统的一部分。 这项研究产生了多个层面的影响。科学地，我们创造了新的矢量和机器 在非侵入性平台上描述单纯性(非纤颤)心律失常的学习策略。 未来的项目将延伸到其他心律失常。临床上，心律失常治疗的个人化 完全远程可穿戴设备可以扰乱当前的顺序护理和资源利用，并提高 偏远和服务不足地区患者的结果。从商业角度来看，这种方法 可以很容易地向医疗保健组织、医生、战略合作伙伴和患者盈利。我们的 团队在这项提案的科学、临床、监管和商业方面经验丰富。

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.