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

心血管疾病是所有人类死亡的主要原因之一。心律失常或心律失常是导致心血管疾病死亡的最常见原因之一。心律失常相关的心脏发病率和死亡率可以通过植入永久性心脏装置来降低，这些装置旨在监测心律是否严重异常，并在必要时立即提供治疗。在美国，每年植入近225,000个起搏器或其他心律管理装置（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，确定心律失常的具体性质是至关重要的。目前，植入式心脏装置只能检测心律失常的存在，而不能识别哪种心律失常。由于目前植入式装置的这一缺陷，它们可能会对心律失常的类型进行错误的纠正措施。缺乏特殊治疗可能会造成毁灭性的后果。因此，能量高效的心律失常检测和识别对下一代植入式心脏装置至关重要。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件。任何新添加的功能都会从电池中消耗更多的电量，并缩短植入心脏设备的电池寿命。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，还将探讨所开发算法的超低功耗硬件实现的可行性。本研究的主要新颖之处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。所开发的人工智能算法硬件可以通过减少计算量和减少计算次数来优化能源效率。该研究结果有望使心脏设备制造商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 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.