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

心血管疾病是世界范围内的头号死因。例如，心力衰竭是美国死亡率的一个重要来源;它占所有急诊室就诊的1%，每9例死亡中就有1例是由心力衰竭引起的。相当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。心脏瓣膜成形术已被证明是一种有效的治疗方法，可改善心脏功能，并降低某些患者的死亡率。然而，大量患者对起搏治疗无效，通常是由于起搏器电极导线放置不当。目前还没有起搏器系统能够提供多部位时间和空间精确起搏的“潜在”受益。为了实现消除心血管疾病的大胆目标，必须开发新技术，以前所未有的时间和空间精度监测患病心脏，并管理心脏起搏以恢复心脏的健康功能。该项目将处理从多个部位记录的数据，并将实时生成针对患者的起搏治疗。该奖项重新审视了起搏器的核心基础，以开发多个部位的时间和空间精确起搏，以实现起搏。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图（IEGM）数据库。重点将是识别临床上难以区分的具有挑战性的病例，因此，能够获得能够指导整体算法开发的最大好处。此信息将添加到关联的元数据文件中。将从至少150例患者中收集数据，每种确定的病理生理学至少有50例患者。将根据多个心内记录部位的数据确定每例患者的病理学。所提出的机器学习管道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。该管道对时间序列数据进行聚类，以识别特定患者的正确病理。此外，该项目还探索了作为专用集成电路（ASIC）的实现，该专用集成电路将植入皮下，以连续处理心内多部位记录，并生成时间和空间精确的起搏模式。总体思路是，通过整合并联合优化算法、电路和架构级创新，全面开发一种方法，解决机器学习和信号处理算法的极低功耗实现问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响力评审标准进行评估，被认为值得支持。

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.