Arrhythmia Detection Using a Smart Phone

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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.