Arrhythmia Detection Using a Smart Phone

描述（由申请人提供）：房颤（AF）是全球最常见的持续性心律失常。尽管在过去十年中出现了新的AF治疗策略，但临床医生和研究人员面临的主要挑战是AF的阵发性，通常是短暂的，并且经常是无症状的。鉴于阵发性和无症状的AF是一个日益严重的临床和公共卫生问题，需要更好，更便宜，更容易获得的AF检测技术。因此，迫切需要开发用于容易接近的监测设备的方法以及准确的AF检测算法，以便改善患者护理并降低与治疗这些心律失常及其并发症相关联的医疗保健成本。为此，我们之前已经开发了灵敏的、实时可实现的算法，用于使用市售的、临床适用的心电图记录进行准确的AF检测。我们还对算法进行了改进，使其能够检测短至12次心跳的AF发作。此外，我们最近开发了一种智能手机应用程序来测量心脏间隔系列，其可用于真实的实时检测AF。鉴于智能手机的日益普及，我们使用智能手机进行AF检测的方法将使患者以及医疗保健提供者有机会在医生办公室和患者家中以外的各种条件下监测AF。由于我们的方法不涉及单独的ECG传感器，而是仅使用标准的智能手机硬件，因此具有成本效益，从而使患者更好地接受和使用。我们的移动的健康房颤检测平台有可能显著改变传统的房颤医疗服务，实现更频繁、快速和患者导向的房颤检测。我们的AF原型使用2分钟的iPhone 4s记录，在马萨诸塞州大学医学中心心脏电生理实验室接受电击治疗的76名已知持续性AF受试者中，灵敏度为99%，特异性为97%。虽然我们的算法对于AF检测是稳健的，但是主要的限制是它不是设计用于区分室性早搏（PVC）和房性早搏（PAC）与AF。因此，该R15项目的目的是增强我们的实时可实现AF算法，用于准确检测和区分正常窦性心律、AF、PVC和PAC;这些能力目前还不具备。我们相信，这项研究将导致快速转化为创新的房颤检测解决方案，从而更有效地监测和诊断AF。最后，拟议的工作有可能显着降低医疗成本，并通过准确和快速地建立高危人群的房颤诊断，从而为临床医生提供机会，以防止这些危及生命的心律失常的继发性并发症，提高患者护理。

DESCRIPTION (provided by applicant): Atrial Fibrillation (AF) is the most common sustained dysrhythmia worldwide. Although new AF treatment strategies have emerged over the last decade, a major challenge facing clinicians and researchers is the paroxysmal, often short-lived, and frequently asymptomatic nature of AF. Given that paroxysmal and asymptomatic AF is a growing clinical and public health problem, better, cheaper, and more readily available AF detection technology is needed. There is, therefore, a pressing need to develop methods for readily-accessible monitoring device as well as an accurate AF detection algorithm in order to improve patient care and reduce healthcare costs associated with treating these arrhythmias and their complications. To this end, we have previously developed sensitive, real-time realizable algorithm for accurate AF detection using commercially available, clinically applicable electrocardiographic recordings. We have also made improvement to the algorithm so that it can detect AF episode that is as short as 12 beats. Further, we have recently developed a smart phone application to measure heart interval series which can be used to detect AF in real time. Given the ever-growing popularity of smart phones, our approach to AF detection using a smart phone will give patients as well as health care providers the opportunity to monitor AF under a wide variety of conditions outside of the physician's office and outside of the patient's home. Because our approach does not involve a separate ECG sensor but instead uses only standard smart phone hardware, it is cost-effective, thereby leading to better acceptance and use by patients. Our mobile health for AF detection platform has the potential to markedly change the traditional delivery of AF healthcare, allowing for more frequent, rapid, and patient-directed AF detection. Our AF prototype using 2 minutes of iPhone 4s recordings has demonstrated a sensitivity of 99% and specificity of 97% on 76 subjects with known persistent AF who underwent electrical at the University of Massachusetts Medical Center Cardiac Electrophysiology Laboratory. Although our algorithm is robust for AF detection, a major limitation is that it is not designed to discriminate premature ventricular contractions (PVC) and premature atrial contractions (PAC) from AF. Hence, the objective of this R15 project is to enhance our real-time realizable AF algorithm for accurate detection of, and discrimination between, normal sinus rhythm, AF, PVCs, and PACs; capabilities that are not yet available. We believe this research will result in rapid translation into innovative AF detection solutions, leading to more effective monitoring and diagnosis of AF. Finally, the proposed work has the potential to significantly reduce healthcare costs and enhance patient care by accurately and rapidly establishing the diagnosis of AF in at-risk groups, thereby providing clinicians with an opportunity to prevent secondary complications of these life-threatening arrhythmias.