SCH: Explainable Learning of Heart Actions from Pulse to Broaden Cardiovascular Healthcare Access

SCH：通过脉搏了解心脏活动的可解释性学习，以扩大心血管医疗保健的可及性

• 批准号: 2124291
• 负责人: Min Wu
• 金额: $ 120万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124291&HistoricalAwards=false
• 关键词: SCH; Explainable; Learning; Heart; Actions

Cardiovascular disease is the most prevalent cause of death.  Early treatment can effectively reduce the risk of sudden cardiac death, but a many cardiac issues show no obvious symptoms in the early stage and would benefit from long-term continuous cardiac monitoring to capture the intermittent and asymptomatic abnormalities of the heart. This disproportionately affects low-income and disadvantaged populations, who have limited access to affordable preventive care.  An electrocardiogram (ECG) is a non-invasive gold standard for diagnosing cardiovascular diseases. Although it is currently possible to obtain an instant ECG test through a special smartwatch or special attachment to a smartphone, these current options require continuous user participation and are impractical to meet the needs of long-term continuous monitoring. This project investigates a new Artificial Intelligence (AI) powered health solution to automated and continuous cardiac monitoring by inferring ECG from the readily available continuous measurements, such as those sharing the same principles as in many wearable devices.  The research from this project will provide insights on how to transfer the ECG-based rich knowledge base to the diagnosis of cardiovascular diseases from wearable sensors. In order to broaden participation and impact, the project will integrate research and educational activities.  These include supporting the workforce development in such in-demand technical areas as machine learning and smart health, and actively engaging students in hands-on and exploratory interdisciplinary research, especially those from the under-represented groups.  The project will contribute to promoting national health, welfare, and prosperity.The key research issues of inferring ECG from photoplethysmogram (PPG), which can be monitored continuously without constant user attention, include: (1) how to apply biomedical insights to model the relations between ECG and PPG;  (2) how to carry out explainable learning for inferring ECG from PPG;  (3) how to make a transformative expansion of public health knowledge based on the newly developed bridge between ECG and PPG; and (4) how to address a variety of diverse and practical conditions, including population diversity, disease progression, and noise/distortions in real-world PPG sensing sources.  The investigator team plans to carry out the core inference from PPG to ECG in several stages, starting with modeling the biophysical relation between ECG and PPG and representing both waveform families through the well-understood basis in the Fourier family as a proof-of-concept.  The team plans to utilize data next to refine the representation using dictionary learning, and incorporate a deep model when extensive data can be leveraged to provide a refined inference.  The bridge between ECG and PPG enabled by explainable AI can bring unprecedented opportunities to expand smart health knowledge to benefit public health.  The investigator team will work closely with a medical expert to explore AI-enabled understanding and promotion of cardiovascular health in exercise physiology, and transferring rich ECG medical knowledge base to the more user-friendly PPG domain.  The team plans to embrace the opportunity of cross-disciplinary collaboration to evaluate the new capabilities in practical settings as well as promote participation and feedback from a diverse population.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

心血管疾病是最普遍的死亡原因。早期治疗可以有效地降低心脏突然死亡的风险，但是许多心脏问题在早期阶段没有明显的症状，并且会受益于长期连续的心脏监测，以捕获心脏间歇性和不对称的异常。这不成比例地影响低收入和弱势群体，他们获得负担得起的预防保健。心电图（ECG）是用于诊断心血管疾病的非侵入性金标准。尽管目前可以通过特殊的智能手表或智能手机的特殊附件获得即时的心电图测试，但这些当前选项需要连续的用户参与，并且不切实际，以满足长期连续监控的需求。该项目通过从随时可用的连续测量中推断出ECG来调查一种新的人工智能（AI）动力健康解决方案，以自动化和连续的心脏监测，例如在许多可穿戴设备中共享相同原理的心电图。该项目的研究将提供有关如何将基于ECG的丰富知识库转移到可穿戴传感器的心血管疾病诊断的见解。为了扩大参与和影响，该项目将整合研究和教育活动。其中包括支持机器学习和智能健康等需求技术领域的劳动力发展，并积极吸引学生进行动手和探索性的跨学科研究，尤其是来自代表性不足的小组的研究。该项目将有助于促进国家健康，福利和繁荣。从光杀解物学（PPG）推断ECG的关键研究问题，可以连续监控而无需持续的用户注意力，包括：（1）如何应用生物医学见解来模拟ECG和PPG之间的关系； （2）如何从PPG中推断ECG进行可解释的学习； （3）如何基于ECG和PPG之间新开发的桥梁进行公共卫生知识的变革性扩展； （4）如何解决各种多样性和实际条件，包括人口多样性，疾病进展以及现实世界中PPG敏感性来源的噪声/扭曲。研究人员团队计划在几个阶段进行从PPG到ECG的核心推断，从对ECG和PPG之间的生物物理关系进行建模，并通过在Fourier家族的良好理解基础上代表两个波形家庭作为概念证明。该团队计划利用旁边的数据使用字典学习来完善表示形式，并在可以利用大量数据以提供精致推断时合并一个深层模型。可解释的AI启用的ECG和PPG之间的桥梁可以带来前所未有的机会来扩大智能健康知识以使公共卫生受益。调查员团队将与医学专家紧密合作，以探索对运动生理学中心血管健康的理解和促进，并将丰富的ECG医学知识基础转移到更具用户友好的PPG领域。该团队计划接受跨学科合作的机会，以评估实践环境中的新能力，并促进来自多样性人群的参与和反馈。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估NSF的法定任务。

项目成果

Cross-Domain Joint Dictionary Learning for ECG Inference From PPG

• DOI: 10.1109/jiot.2022.3231862
• 发表时间: 2021-01
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Xin Tian;Qiang Zhu;Yuenan Li;Min Wu