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EAGER: Exploring Artificial Intelligence Techniques for Energy-Efficient Arrhythmia Detection and Identification in Connected Implantable Cardiac Devices

EAGER：探索人工智能技术，在互联的植入式心脏设备中进行节能心律失常检测和识别

基本信息

批准号：
2041327
负责人：
Eugene John
金额：
$ 28.6万
依托单位：
University of Texas at San Antonio
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041327&HistoricalAwards=false
关键词：
EAGER Exploring Artificial Intelligence Techniques

项目摘要

Cardiovascular disease is one of the major causes of all human deaths. Irregular heart rhythms or arrhythmias are one of the most common causes for cardiovascular death. Arrhythmia-related cardiac morbidity and mortality can be reduced by the implantation of permanent cardiac devices that are designed to monitor the heart rhythm for serious abnormalities and to deliver immediate therapy when necessary. In the United States, nearly 225,000 pacemakers or other cardiac rhythm management devices (CRMD) are implanted annually. Device manufacturers have recently started incorporating advanced features to make the cardiac devices smarter and more connected. In the treatment of arrhythmia, the identification of the specific nature of arrhythmia is critical. At present the implantable cardiac devices have the capability only to detect the presence of an arrhythmia not to identify which kind of arrhythmia. Because of this shortcoming in current implantable devices, it is possible that they may administer the wrong corrective measure for the type of arrhythmia. This lack of specific treatment can have devastating consequences. Thus, energy efficient arrhythmia detection and identification is critical to next generation implantable cardiac devices. The results of this research work will have the potential to significantly impact the way patients suffering from arrhythmia are diagnosed and treated. CRMDs are severely energy constrained devices. Any new added features will drain more charge from the battery and will reduce the battery life of the implanted cardiac devices. In this research, the team will explore energy-efficient novel artificial intelligence (AI) techniques for real-time detection and identification of arrhythmia in connected smart implantable cardiac devices. Further, the feasibility of ultra-low power hardware implementation of the developed algorithms will be explored. The main novelty of this research lies in using statistical models and algorithms and in using energy efficient deep neural networks (DNNs) for arrhythmia detection and identification. The developed AI algorithm hardware can be optimized for energy efficiency by reduction of computation size and by reduction of number of computations. The research results are expected to enable the cardiac device manufacturers to develop the next generation of implantable cardiac devices capable of identifying arrythmias.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.

心血管疾病是所有人类死亡的主要原因之一。心律不齐是心血管死亡的最常见原因之一。通过植入永久性心脏装置，可降低心律失常相关的心脏病发病率和死亡率，永久性心脏装置旨在监测心律是否出现严重异常，并在必要时提供即时治疗。在美国，每年植入近225，000个起搏器或其他心律管理设备（CRMD）。设备制造商最近开始整合先进的功能，使心脏设备更智能，连接更紧密。在心律失常的治疗中，对心律失常的特异性性质的识别至关重要。目前，植入式心脏设备仅具有检测心律失常的存在的能力，而不能识别哪种心律失常。由于当前植入式器械的这一缺点，它们可能会对心律失常类型实施错误的纠正措施。缺乏具体的治疗可能会产生破坏性的后果。因此，能量高效的心律失常检测和识别对于下一代植入式心脏设备至关重要。这项研究工作的结果将有可能显著影响心律失常患者的诊断和治疗方式。CRMD是能量严重受限的器械。任何新增加的功能都将消耗更多的电池电量，并将缩短植入心脏器械的电池寿命。在这项研究中，该团队将探索节能的新型人工智能（AI）技术，用于实时检测和识别连接的智能植入式心脏设备中的心律失常。此外，超低功耗硬件实现的算法的可行性进行了探讨。这项研究的主要新奇在于使用统计模型和算法，以及使用节能深度神经网络（DNN）进行心律失常检测和识别。开发的AI算法硬件可以通过减少计算大小和减少计算次数来优化能源效率。该研究成果有望使心脏设备制造商能够开发出能够识别心律失常的下一代植入式心脏设备。该奖项反映了NSF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。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence

DOI：
10.1109/cvpr46437.2021.01347
发表时间：
2020-11
期刊：
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
0
作者：
Chengyue Gong;Dilin Wang;Qiang Liu
通讯作者：
Chengyue Gong;Dilin Wang;Qiang Liu

A Meta-Transfer Learning Approach to ECG Arrhythmia Detection

DOI：
10.1109/embc48229.2022.9871518
发表时间：
2022-07
期刊：
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
影响因子：
0
作者：
Wuxiao Chen;T. Banerjee;E. John
通讯作者：
Wuxiao Chen;T. Banerjee;E. John

Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks