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

Debatable issues in automated ECG reporting

Although automated ECG analysis has been available for many years, there are some aspects which require to be re-assessed with respect to their value while newer techniques which are worthy of review are beginning to find their way into routine use. At the annual International Society of Computerized Electrocardiology conference held in April 2017, four areas in particular were debated. These were a) automated 12 lead resting ECG analysis; b) real time out of hospital ECG monitoring; c) ECG imaging; and d) single channel ECG rhythm interpretation. One speaker presented the positive aspects of each technique and another outlined the more negative aspects. Debate ensued. There were many positives set out for each technique but equally, more negative features were not in short supply, particularly for out of hospital ECG monitoring

How often should we monitor for reliable detection of atrial fibrillation recurrence? Efficiency considerations and implications for study design

OBJECTIVE: Although atrial fibrillation (AF) recurrence is unpredictable in terms of onset and duration, current intermittent rhythm monitoring (IRM) diagnostic modalities are short-termed and discontinuous. The aim of the present study was to investigate the necessary IRM frequency required to reliably detect recurrence of various AF recurrence patterns. METHODS: The rhythm histories of 647 patients (mean AF burden: 12±22% of monitored time; 687 patient-years) with implantable continuous monitoring devices were reconstructed and analyzed. With the use of computationally intensive simulation, we evaluated the necessary IRM frequency to reliably detect AF recurrence of various AF phenotypes using IRM of various durations. RESULTS: The IRM frequency required for reliable AF detection depends on the amount and temporal aggregation of the AF recurrence (p<0.0001) as well as the duration of the IRM (p<0.001). Reliable detection (>95% sensitivity) of AF recurrence required higher IRM frequencies (>12 24-hour; >6 7-day; >4 14-day; >3 30-day IRM per year; p<0.0001) than currently recommended. Lower IRM frequencies will under-detect AF recurrence and introduce significant bias in the evaluation of therapeutic interventions. More frequent but of shorter duration, IRMs (24-hour) are significantly more time effective (sensitivity per monitored time) than a fewer number of longer IRM durations (p<0.0001). CONCLUSIONS: Reliable AF recurrence detection requires higher IRM frequencies than currently recommended. Current IRM frequency recommendations will fail to diagnose a significant proportion of patients. Shorter duration but more frequent IRM strategies are significantly more efficient than longer IRM durations. CLINICAL TRIAL REGISTRATION URL: Unique identifier: NCT00806689

Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study

Peer reviewe

Long-term Arrhythmia Monitoring in Cryptogenic Stroke: Who, How, and for How Long?

Cryptogenic stroke and transient ischemic attack (TIA) account for approximately one-third of stroke patients [1]. Paroxys-mal atrial fibrillation (PAF) has been suggested as a major etiology of these cryptogenic strokes [2, 3]. PAF can be difficult to diagnose because it is intermittent, often brief, and asymptomatic. PAF might be more prevalent than persistent atrial fibrillation in stroke and TIA patients, especially in younger populations [4, 5]. In patients with atrial fibrillation, anticoagulation provides significant risk reduction [6]. A new generation of oral anticoagulants has been approved for non-valvular atrial fibrillation, providing a variety of therapeutic options for patients with atrial fibrillation and risk of stroke [7]. Prior practice included an admission electrocardiogram (ECG) and continuous telemetry monitoring while in hospital [8]. However, this approach can lead to under-detection of brief asymptomatic events, which can occur at variable intervals, often outside of the hospital setting. Technological advancements have led to devices that can monitor cardiac rhythms outside of the hospital for longer durations resulting in higher yield of detection of atrial fibrillation events. Moreover, recent studies show that the normal monitoring time for arrhythmias may be shorter than ideal in order to detect atrial fibrillation, and increasing this interval could significantly improve detection of atrial fibrillation in these patients [9, 10]. The aim of this study is to review the literature in order to define what subgroup of patients, with what methodologies, and for how long monitoring for atrial fibrillation should occur in patients presenting with cryptogenic stroke

Diagnostyka elektrokardiograficzna w warunkach SOR za pomocą mobilnego jednoodprowadzeniowego urządzenia EKG

Introduction. Electrocardiography (ECG) is one of the basic diagnostic tests used in emergency departments and byemergency medical services. Life-threatening arrhythmias can be detected using a single-lead ECG. Therefore, single--lead ECG devices can be used for arrhythmia detection, as their availability steadily increases. Kardia Mobile from Alive-Cor is an example of such a device, recording a single-lead ECG and automatically detecting atrial fibrillation (AF) — themost common complex supraventricular tachyarrhythmia. The aim of our study was to evaluate the utility of a single-leadmobile ECG device in detecting AF in medical practice of emergency services. Material and methods. Study included 118 patients (62 women and 56 men) who were hospitalized in a hospitalemergency department and consented to examination with Kardia Mobile immediately after a standard 12-lead ECG.Results of both tests were subsequently compared. Ultimately, 121 different pairs of ECG recordings were analyzed (in3 cases an additional ECG recording was performed after an electrical cardioversion). Results. Sinus rhythm was identified in 99 patients and 22 were diagnosed with AF using a 12-lead ECG (reference).Kardia Mobile correctly detected AF in 19 of 22 patients with AF (sensitivity: 86.4%) and absence of AF in 96 of 99people without AF (specificity: 97%). Conclusions. Kardia Mobile device is effective in automated detection of AF among patients hospitalized in the emergencydepartment.Wstęp. Elektrokardiografia (EKG) jest jednym z podstawowych badań wykorzystywanych w praktyce szpitalnych oddziałów ratunkowych (SOR) i zespołów ratownictwa medycznego. Zaburzenia rytmu mogące powodować zatrzymanie krążenia należą do najważniejszych dla zdrowia pacjenta nieprawidłowości wykrywanych w badaniu EKG. Są one widoczne we wszystkich odprowadzeniach EKG, co pozwala używać do ich wykrywania jednoodprowadzeniowych aparatw EKG, których dostępność na rynku istotnie się zwiększyła. Przykładem takiego urządzenia jest Kardia Mobile firmy AliveCor, zdolne do akwizycji pojedynczego odprowadzenia EKG oraz do automatycznej detekcji rytmu zatokowego oraz migotania przedsionków (AF), najczęściej występującej złożonej tachyarytmii nadkomorowej. Celem pracy jest określenie przydatności mobilnych jednoodprowadzeniowych rejestratorów EKG detekcji AF w warunkach SOR. Materiał i metody. Do badania włączono 118 osób (62 kobiety i 56 mężczyzn) hospitalizowanych na SOR, które wyraziły zgodę na wykonanie badania urządzeniem Kardia Mobile bezpośrednio po wykonaniu standardowego 12-odprowadzeniowego badania EKG. Wyniki obu badań porównywano. Ostatecznie analizowano 121 różnych par odczytów EKG (u 3 pacjentów wykonano akwizycję EKG przed zabiegem kardiowersji elektrycznej i po nim). Wyniki. W 12-odprowadzeniowym (referencyjnym) zapisie EKG rytm zatokowy rozpoznano u 99 badanych, u 22 badanych rozpoznano AF. Urządzenie Kardia Mobile prawidłowo rozpoznało AF u 19 spośród 22 badanych z arytmią (czułość: 86,4%), a brak AF — u 96 z 99 badanych (swoistość: 97%). Wnioski. Urządzenie Kardia Mobile jest w efektywnym narzędziem w diagnostyce AF w warunkach SOR

Can heart rate variability parameters derived by a heart rate monitor differentiate between atrial fibrillation and sinus rhythm?

Background: Heart rate variability (HRV) parameters, and especially RMSSD (root mean squared successive differences in RR interval), could distinguish atrial fibrillation (AF) from sinus rhythm(SR) in horses, as was demonstrated in a previous study. If heart rate monitors (HRM) automatically calculating RMSSD could also distinguish AF from SR, they would be useful for the monitoring of AF recurrence. The objective of the study was to assess whether RMSSD values obtained from a HRM can differentiate AF from SR in horses. Furthermore, the impact of artifact correction algorithms, integrated in the analyses software for HRV analyses was evaluated. Fourteen horses presented for AF treatment were simultaneously equipped with a HRM and an electrocardiogram (ECG). A two-minute recording at rest, walk and trot, before and after cardioversion, was obtained. RR intervals used were those determined automatically by the HRM and by the equine ECG analysis software, and those obtained after manual correction of QRS detection within the ECG software. RMSSD was calculated by the HRM software and by dedicated HRV software, using six different artifact filters. Statistical analysis was performed using the Wilcoxon signed-rank test and receiver operating curves. Results: The HRM, which applies a low level filter, produced high area under the curve (AUC) (>0.9) and cut off values with high sensitivity and specificity. Similar results were obtained for the ECG, when low level artifact filtering was applied. When no artifact correction was used during trotting, an important decrease in AUC (0.75) occurred. Conclusion: In horses treated for AF, HRMs with automatic RMSSD calculations distinguish between AF and SR. Such devices might be a useful aid to monitor for AF recurrence in horses