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

心血管疾病是最普遍的死亡原因。早期治疗可以有效降低心源性猝死的风险，但许多心脏问题在早期没有明显的症状，需要长期持续的心脏监测来捕捉心脏的间歇性和无症状异常。这对低收入和弱势群体的影响尤为严重，他们获得负担得起的预防保健的机会有限。心电图（ECG）是诊断心血管疾病的无创金标准。虽然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心电图测试，但目前这些选项需要用户持续参与，无法满足长期连续监测的需求。该项目研究了一种新的人工智能（AI）驱动的健康解决方案，通过从现成的连续测量中推断心电图，实现自动化和连续的心脏监测，例如与许多可穿戴设备共享相同的原理。该项目的研究将为如何将基于ecg的丰富知识库转移到可穿戴传感器的心血管疾病诊断提供见解。为了扩大参与和影响，该项目将整合研究和教育活动。这些措施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，并积极让学生参与动手和探索性跨学科研究，特别是那些来自代表性不足群体的学生。该项目将有助于促进国民健康、福利和繁荣。利用光电容积描记图（photoplethysmogram， PPG）进行心电图推断的关键研究问题包括：(1)如何应用生物医学的见解建立ECG与PPG之间的关系模型；(2)如何进行可解释学习，从PPG推断心电；(3)如何基于ECG和PPG之间新建立的桥梁进行公共卫生知识的变革性扩展；以及(4)如何解决各种各样的实际条件，包括现实世界PPG传感源中的种群多样性、疾病进展和噪声/扭曲。研究小组计划分几个阶段进行从PPG到ECG的核心推断，首先是对ECG和PPG之间的生物物理关系进行建模，并通过傅立叶族中充分理解的基础来表示两个波形族，作为概念验证。该团队计划接下来利用数据来使用字典学习来改进表示，并在可以利用大量数据来提供精确推理时合并一个深度模型。可解释的人工智能实现了ECG和PPG之间的桥梁，可以为扩展智能健康知识带来前所未有的机会，从而造福公众健康。研究团队将与医学专家密切合作，探索人工智能在运动生理学中对心血管健康的理解和促进，并将丰富的ECG医学知识库转移到更友好的PPG领域。该团队计划利用跨学科合作的机会来评估实际环境中的新能力，并促进不同人群的参与和反馈。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.