Non-invasive Condition Monitoring of Ventricular Assistive Devices Using Automated Advanced Acoustic Methods

项目摘要 该研究项目的主要目标是创造一种新的方法来确定健康状况 植入心室辅助装置（VAD），只使用智能电子听诊器和 单导联心电图VAD是永久植入左心室功能不良患者体内的泵。他们 以设定的速度运行，几乎没有内部自动速度调节。因此，这是一个复杂的问题 对于医疗保健提供者来说，诊断VAD患者的症状可能是或可能不是由于 降低泵性能。当前最先进的技术需要训练有素的医疗保健提供者， 使用昂贵的临床设备，有时需要侵入性或部分侵入性手术， 确定症状是否可能是由于VAD设备引起的，更不用说具体问题是什么。 我们假设通过使用先进的信号处理和机器学习 技术，我们可以对VAD患者的健康状况进行分类，因为它与VAD的机械功能有关。 性能和血流动力学流入正常和功能障碍状态。 为了验证这一假设，我们将修改现有的血流动力学模拟器，不仅创建随时间变化的 压力到VAD输入和输出，但也有心脏瓣膜，创建类似于人类的声学签名 瓣膜，并且可能充盈不足至心室塌陷点（所有情况与常见情况相似 在富含VAD的临床环境中）。我们将具体解决大多数VAD故障模式：（1） VAD流入量相对于泵速，（2）由于全身性高血压或 VAD流动回路内存在闭塞，（3）存在严重的瓣膜疾病（主动脉 反流、二尖瓣反流和二尖瓣狭窄），以及（4）心率和节律对VAD的影响 性能这些条件的各种严重程度将在模拟器中播种，并且音频签名 将记录VAD泵和相关的自然心音。数字心音测量 将使用本方案中开发的新算法进行处理，以了解根本物理原因， 并与所测试的VAD功能/功能障碍模式进行比较。随着项目的进展，实际的患者数据， 研究小组在2018年以非侵入性方式（听诊器，ECG和超声）收集的数据，将被 与相同条件下的模拟结果进行比较。 如果拟议的研究是成功的，有机会：（1）通过检测改善病人护理 在早期阶段的常见临床状况，（2）避免在某些情况下需要先进的诊断，（3） 允许医务人员测试各种VAD设置，以优化输出，而无需高级 诊断，以及（4）允许VAD问题的远程诊断。除了Jason Kolodziej博士，Steven博士 戴博士，王林伟博士，卡尔施瓦茨博士，伊戈尔戈塞夫博士和迈克尔理查兹博士，研究小组将包括 三个高级设计团队（15名UG学生），三到六名本科生，以及一名博士。学生.

PROJECT SUMMARY The primary objective of this research project is to create a new approach to identifying the state-of-health of implanted ventricular assist devices (VAD) using nothing more than a smart electronic stethoscope and a single lead ECG. VADs are pumps permanently implanted into patients with poor left ventricular function. They run at a set speed with almost no internal automatic speed adjustments. For this reason, it is a complex problem for healthcare providers to diagnose VAD patients who present with symptoms that may or may not be due to reduced pump performance. The current state-of-the-art requires very highly trained healthcare providers with access to expensive clinical equipment and sometimes requires invasive or partially invasive procedures just to determine if the symptoms might be due to the VAD device, let alone what the specific problem is. We hypothesize that through the use of advanced signal processing and machine learning techniques, we can classify a VAD patient’s health status as it pertains to the VAD’s mechanical performance and hemodynamic flow into normal and dysfunctional states. To test this hypothesis, we will modify an existing hemodynamic simulator that creates not only time-varying pressure to the VAD input and output, but also has heart valves that create an acoustic signature similar to human valves, and can be underfilled to the point of ventricular collapse (all conditions similar to those commonly seen in a VAD-rich clinical environment). We will specifically address most VAD failure modes: (1) underfilling of the VAD inflow relative to the pump speed, (2) excessive afterload due to either systemic hypertension or the presence of occlusions within the VAD flow circuit, (3) presence of significant valvular disease (aortic regurgitation, mitral regurgitation, and mitral stenosis), and (4) the effect of heart rate and rhythm on VAD performance. Various severity levels of these conditions will be seeded in the simulator, and the audio signature of the VAD pump and associated native heart sounds will be recorded. The digital heart sound measurements will be processed using novel algorithms to be developed in this protocol to understand the root physical cause, and compared to the VAD function/dysfunction mode being tested. As the project proceeds actual patient data, collected by the research team in 2018 in a non-invasive manner (stethoscope, ECG, and ultrasound), will be compared versus the simulator results under the same conditions. If the proposed research is successful there is the opportunity to: (1) improve patient care by detecting common clinical conditions at an early stage, (2) avoid the need for advanced diagnostics in some cases, (3) allow medical personnel to test various VAD settings to optimize output without the need for advanced diagnostics, and (4) allow for remote diagnostics of VAD problems. In addition to Dr. Jason Kolodziej, Dr. Steven Day, Dr. Linwei Wang, Dr. Karl Schwarz, Dr. Igor Gosev, and Dr. Michael Richards, the research team will consist of three senior design teams (15 UG students), three to six undergraduate students, and a Ph.D. student.