SCH: INT: Collaborative Research: Patient Specific Multisite Pacing of Diseased Human Hearts

心血管疾病是全球头号死因。例如，心力衰竭是美国境内死亡的一个重要来源；它导致了1%的急诊室病例，每九例死亡中就有一例是心力衰竭造成的。相当大比例的心力衰竭患者并发传导系统疾病，这将导致他们最终死亡。心脏再同步化已被证明是一种有效的治疗方法，可以改善心功能并降低某些患者的死亡率。然而，相当数量的患者对再同步治疗没有反应，通常是由于起搏器导线放置不当。目前尚无起搏器系统可为再同步提供多部位时间和空间精确起搏的“潜在”益处。为了实现消除心血管疾病的大胆目标，必须开发新的技术，以前所未有的时间和空间精度监测患病的心脏，并管理心脏的起搏，以恢复心脏的健康功能。该项目将处理从多个地点记录的数据，并将实时生成针对患者的起搏疗法。该奖项回顾了起搏器的核心基础，以在多个地点开发时间和空间上精确的起搏，以实现再同步。研究人员将开发一个强大的、注释良好的多个心脏部位的心内电信号(IEGM)数据库。重点将放在识别临床上难以区分的具有挑战性的病例上，因此，能够指导整个算法开发将获得最大的好处。此信息将添加到关联的元数据文件中。数据将从至少150名患者中收集，每种确定的病理生理学至少有50名患者。将根据来自多个心脏内记录点的数据确定每个患者的病理。提出的机器学习流水线使用小波来探索时间序列的表示，然后使用李群框架来学习多个时间序列的变换。这条管道将时间序列数据聚集在一起，以确定特定患者的正确病理。此外，该项目还探索了作为一种专用集成电路(ASIC)的实施，该集成电路将被植入皮下，以连续处理心内多部位记录，并产生时间和空间上精确的起搏模式。总体方法是整体开发一种方法，通过不仅结合算法、电路和体系结构级别的创新，而且联合优化算法、电路和体系结构级别的创新，来解决机器学习和信号处理算法的极低功耗实施问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，认为值得支持。

Cardiovascular disease is the number one cause of death worldwide. For example, heart failure is a significant source of mortality within the U.S; it is responsible for 1% of all emergency room presentations and contributes to one in every nine deaths. A significant proportion of heart failure patients have concurrent conduction system disease, which will lead to their eventual death. Cardiac resynchronization has proven to be a useful therapy for improving cardiac function as well as reducing mortality in some patients. However, a significant number of patients fail to respond to resynchronization therapy, often due to inadequate pacemaker lead placement. There currently exists no pacemaker system that provides the 'potential' benefits of multisite temporally and spatially precise pacing for resynchronization. To reach the audacious goal of eliminating cardiovascular diseases, new technologies must be developed that will monitor the diseased heart with unprecedented temporal and spatial precision and will manage the pacing of the heart to restore a healthy function of the heart. This project will process data recorded from multiple sites and will generate a pacing therapy specific to the patient in real-time.The award revisits the core foundation of pacemakers to develop temporally and spatially precise pacing at multiple sites for resynchronization. The researchers will develop a robust, well-annotated database of intra-cardiac electrograms (IEGM) from multiple cardiac sites. The focus will be on identifying challenging cases that are clinically difficult to differentiate and, thus, stand to reap the greatest benefit of being able to direct overall algorithm development. This information will be added to the associated metadata file. Data will be collected from a minimum of 150 patients with at least 50 patients from each identified pathophysiology. Pathology of each patient based on data from multiple intra-cardiac recording sites will be identified. The proposed machine-learning pipeline explores the representation of time series using wavelets and then learns transformations of multiple time series using the Lie group framework. This pipeline clusters time-series data to identify the right pathology for the specific patient. In addition, the project explores implementation as an application specific integrated circuit (ASIC) that will be implanted subcutaneously to continuously process intracardiac multi-site recordings as well as to generate temporally and spatially precise pacing patterns. The overall approach is to holistically develop a methodology that can address an extremely low-power implementation of machine learning and signal processing algorithms, by not only combining, but jointly optimizing, algorithmic-, circuit- and architecture-level innovations.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.