Feasibility testing of a novel AI-enabled, cloud-based ECG diagnostic solution to enable fast and affordable diagnosis in long-term continuous ambulatory ECG monitoring

项目总结。拟议的观察性研究是为了评估新型心电监护仪的可行性。 系统利用并发人工智能和云技术实现长期持续监控(LTcm) 临床环境。它不打算使用来自调查解决方案的任何数据或信息来干预， 干预或影响为参与者做出的任何临床决定。在每年近200万人晕厥或 TIA/卒中患者，12-15%与心律失常相关，这通常具有较高的长期风险 致残率甚至死亡率高于其他病因的患者。适当的风险分层和及早启动 适当的预防性治疗可以显著减少心脏相关疾病及其 相关死亡率。尽管已被证明能够以高诊断率检测心律失常， 目前的护理标准有重大的市场痛苦：1)交付最终报告的线下延迟数天至数周 扩展Holter；2)在线移动心脏遥测的流性心律失常检测精度较低；以及3) 医生无法访问患者的心电数据。ZBeats的解决方案旨在提高当今的 通过解决技术可获得性和可负担性来提供护理。ZBPro™，ZBeats的阿尔法原型得到验证 针对我们的专有数据集以及ANSI/AAMIEC57中要求的公共数据集，演示 算法、数据传输和可视化都工作得很好。在这项第一阶段研究中，可行性将 通过完成以下特定目标(SA)在临床环境中进行测试：SA1：设置数据收集 并在招聘前对临床人员进行培训。SA2：引导患者的可接受性 通过招募60-75名患者来评估佩戴该设备长达7天的时间。SA3：评估心律失常捕获 通过在审查由以下方面生成的报告后进行医生满意度问卷来提高能力 学习系统。SA4：进行数据分析，并开始设计第二阶段研究的方案。这项提议将 进行ZBeats、石溪大学医院和兰克瑙医疗中心的合作。长的- 学期目标是通过减少检测到的时间来显著提高长期中医药的当前护理标准 对于心脏相关的高危患者，危及生命的心律失常从几周增加到几分钟 通过利用人工智能算法、云基础架构和低成本 柔性材料贴片。这种成本降低将导致更一般的医疗用例，例如远程医疗和 远程患者监护(RPM)，使更广泛的人群受益。

PROJECT SUMMARY. The proposed observational study is to evaluate the feasibility of a novel ECG monitoring system leveraging concurrent AI and cloud technologies in long-term continuous monitoring (LTCM) in the clinical environment. It does not intend to use any data or information from the investigational solution to interfere, intervene or affect any clinical decisions made for the participants. Among nearly 2M per year syncope or TIA/stroke patients, 12-15% are cardiac-arrhythmia associated, which usually carries higher risk for long-term disability and even mortality than other-etiologies patients. Proper risk stratification and early initiation of appropriate preventative treatment can result in significant reduction of the cardiac related diseases and their associated mortality. Although LTCM has been proven to be able to detect arrhythmia with high diagnostic yield, the current standard of care has major market pains: 1) days-to-weeks of delay to deliver final report for offline extended Holter; 2) low accuracy in stream arrhythmia detection for online Mobile Cardiac Telemetry; and 3) physicians do not have access to patients’ ECG data. ZBeats’ solution is aiming to improve today’s standard of care by addressing technology accessibility and affordability. ZBPro™, ZBeats’ alpha prototype was validated against our proprietary dataset as well as public datasets required in ANSI/AAMI EC57, demonstrating algorithms, data transmission and visualization work well as expected. In this Phase I study, the feasibility will be tested in the clinical environment by completing the following specific aims (SA): SA1: setup data collection systems and provide training to clinical personnel prior to recruitment. SA2: Conduct patients’ acceptability evaluation by enrolling 60-75 patients to wear the device for up to 7 days. SA3: Evaluate the arrhythmia-capturing capability by conducting physician’s satisfaction questionnaires after reviewing the reports generated from the study system. SA4: Conduct data analysis and start designing the protocol for Phase II study. This proposal will undergo collaboration among ZBeats, Stony Brook University Hospital and Lankenau Medical Center. The long- term goal is to dramatically improve the current standard of care in LTCM by reducing the time to detection of life-threatening arrhythmia from weeks to minutes for cardiac-related high-risk patients, increase the streaming detection accuracy and reducing the total costs by leveraging AI algorithms, cloud infrastructure and a low-cost flexible-material patch. This cost reduction will lead to more general medical use cases, such as telehealth & Remote Patient Monitoring (RPM) to benefit broader population.