Can smartphone wireless ECGs be used to accurately assess ECG intervals in pediatrics? A comparison of mobile health monitoring to standard 12-lead ECG

BACKGROUND:Arrhythmias in children are often paroxysmal, complicating the ability to capture the abnormal rhythm on routine ECG during an outpatient visit. The Alivecor Kardia Mobile (KM) device is a wireless mobile health (mHealth) device that generates a single lead ECG tracing with a FDA-approved algorithm for detection of atrial fibrillation in adults. OBJECTIVE:The goal of this study is to assess the accuracy of interval measurements on KM tracings by directly comparing to standard 12-lead ECGs in pediatric patients. METHODS:This single center, prospective study enrolled pediatric outpatients, age 20ms with 4/9 (44%) having a conduction disorder and 2/9 (22%) having marked sinus arrhythmia. Bland-Altman method of agreement demonstrated strong agreement for QRSd and QTc. The AF algorithm reported 4/30 (13%) false positive "possible AF" diagnoses (rhythm over-read on KM demonstrated n = 3 marked sinus arrhythmia, n = 1 sinus rhythm with aberrated PACs) resulting in a specificity of 87%. CONCLUSION:The Alivecor Kardia device produces accurate single lead ECG tracings in both healthy children and children with cardiac disease or rhythm abnormalities across the pediatric spectrum. This mHealth application provides an accurate, non-invasive, real-time approach for ambulatory ECG monitoring in children and adolescents

Electrocardiographic patch devices and contemporary wireless cardiac monitoring.

Cardiac electrophysiologic derangements often coexist with disorders of the circulatory system. Capturing and diagnosing arrhythmias and conduction system disease may lead to a change in diagnosis, clinical management and patient outcomes. Standard 12-lead electrocardiogram (ECG), Holter monitors and event recorders have served as useful diagnostic tools over the last few decades. However, their shortcomings are only recently being addressed by emerging technologies. With advances in device miniaturization and wireless technologies, and changing consumer expectations, wearable “on-body” ECG patch devices have evolved to meet contemporary needs. These devices are unobtrusive and easy to use, leading to increased device wear time and diagnostic yield. While becoming the standard for detecting arrhythmias and conduction system disorders in the outpatient setting where continuous ECG monitoring in the short to medium term (days to weeks) is indicated, these cardiac devices and related digital mobile health technologies are reshaping the clinician-patient interface with important implications for future healthcare delivery

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure

Heart disease and stroke are becoming the leading cause of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECG have limited accuracy and rely on external resources to analyze the signal and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behavior. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud and (c) the overall power consumption. Based on this concept, the HEART platform is presented that combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate and instant monitoring of the heart. The performance of the system is evaluated concerning the accuracy of detecting abnormal events and the power consumption of the wearable device. Results indicate that a very high percentage of success can be achieved in terms of event detection ratio and the device being operative up to a several days without the need for a recharge

Computer Aided ECG Analysis - State of the Art and Upcoming Challenges

In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and rhythm-based analysis. There are some advancements in particular ECG segments detection and beat classifications but with limited evaluations and without clinical approvals. This paper presents state of the art advancements in those areas till present day. Besides this short computer science and signal processing literature review, paper covers future challenges regarding the ECG signal morphology analysis deriving from the medical literature review. Paper is concluded with identified gaps in current advancements and testing, upcoming challenges for future research and a bullseye test is suggested for morphology analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on computer as a tool, 1-4 July 2013, Zagreb, Croati

Evaluating the Impact of KardiaMobile on Healthcare Expenditures

Background: KardiaMobile is a mobile ECG device, with smartphone connectivity, that is able to detect atrial fibrillation (a-fib). The device captures a 30 second reading and an algorithm differentiates a-fib from sinus rhythm. The mobile application can alert the user to an a-fib reading and also send the ECG output to their personal physician. By monitoring a-fib without accessing traditional care, KardiaMobile has the potential to reduce healthcare costs for patients with a-fib. This study looked to evaluate the effect of KardiaMobile on healthcare utilization for patients with suspected or diagnosed a-fib and who see a clinical cardiac electrophysiologist. Methods: To measure utilization, we conducted a retrospective chart review. A database was created documenting the number of times patients went to office visits, the emergency department, were admitted to the hospital, had a cardiac procedure done, called/messaged the office, used cardiac monitors, and had cardiac imaging conducted. Each of these events were counted for one year prior to the first time the patient used KardiaMobile, and up to one year after they began using KardiaMobile. Results: Comparing the pre-Kardia and current-Kardia timeframes, we observed a minor overall difference in healthcare utilization. Looking at individual points of contact with the healthcare system, there was a reduction in emergency department visits. Conclusion: The use of KardiaMobile in patients with diagnosed or suspected a-fib was not associated with any difference in overall healthcare utilization. The data show that there was a significant reduction in higher acuity points of care--notably emergency department visits

Sensor, Signal, and Imaging Informatics in 2017.

Objective To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.Methods PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.Results The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.ConclusionThe growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics

Clinical and Procedural Effects of Transitioning to Contact Force Guided Ablation for Atrial Fibrillation.

Background: A major innovation in atrial fibrillation (AF) ablation has been the introduction of contact force (CF) sensing catheters. Objective: To evaluate procedural and clinical effects of transitioning to CF-guided AF ablation. Methods: Consecutive AF ablation patients were studiedduring the period of time of transitioning from a non-CF to CF sensing catheter. Procedural data recorded was total radiofrequency time, time to isolate the left pulmonary veins (LPVs), and time to isolate the right pulmonary veins (RPVs). Clinically, the 3 and 12-month maintenance of sinus rhythm was noted and compared by: paroxysmal vs. persistent AF; CT scan LA volume more or less than 150 cc; CHA2DS2VASC more or less than 2; and LVEF more or less than 55%. Safety data was recorded as well. Results: Total ablation times were shorter (113 vs.146 min, p=0.011)when using the CF catheters compared to non-CF ablations. This was driven by a decrease in both LPV (46 vs.72 min, p\u3c0.001) and RPV time (54 vs. 75 min, p=0.002).The use of CF catheter did not change the overall percentage of patients in sinus rhythm at 3 and 12-months of follow up. However, sinus rhythm was more frequent at 12 months with CF ablation inpatients with an LA volume of more than 150 cc when compared to non-CF ablation (84.6% and 52.4%, p=0.03). There was no difference in outcomes with stratification by CHA2DS2VASC score or LVEF. No significant difference in complications was noted. Conclusions: For AF ablation, the initial use of CF-sensing technology reduced procedure times with similar overall sinus rhythm maintenance at 3 and 12 months. CF improved 12-month outcomes in patients with an enlarged LA

Review of sensors for remote patient monitoring

Remote patient monitoring (RPM) of physiological measurements can provide an efficient method and high quality care to patients. The physiological signals measurement is the initial and the most important factor in RPM. This paper discusses the characteristics of the most popular sensors, which are used to obtain vital clinical signals in prevalent RPM systems. The sensors discussed in this paper are used to measure ECG, heart sound, pulse rate, oxygen saturation, blood pressure and respiration rate, which are treated as the most important vital data in patient monitoring and medical examination