Automatic Detection Of Atrial Fibrillation Research Articles

Characterizing Atrial Fibrillation in Empirical Mode Decomposition Domain

Automatic patient monitoring for cardiac diseases requires the detection of various abnormal electrical activities occurring in the heart. Pseudo-pacemaker activities by cardiac cells or non-standard electrical impulses in the atria or ventricle cause various types of arrhythmia. Atrial fibrillation (AF) is the most important irregularity that occurs due to electrical malfunctioning in the atria. Unsynchronized pumping of the atria and ventricle causes different volumes of blood flow at different rhythms. In this paper, a method for the automatic detection of AF in the empirical mode decomposition (EMD) domain is proposed. Being a completely data-adaptive technique, EMD can be applied to all kinds of electrocardiogram signals. Selective reconstruction eliminates the requirement of conventional preprocessing for denoising the raw data. A combination of temporal and statistical features is used to characterize abnormal rhythms. A novel SQ time deviation feature is introduced, which is proved to be a good choice for AF classification. A nonlinear-kernel-based support vector machine classifier is used for classification. Sensitivity, specificity, and accuracy comparable to those of previous works are achieved for the MIT-BIH Arrhythmia Database.

Yield of screening for atrial fibrillation in primary care with a hand-held, single-lead electrocardiogram device during influenza vaccination.

AimsTo assess the yield of screening for atrial fibrillation (AF) with a hand-held single-lead electrocardiogram (ECG) device during influenza vaccination in primary care in the Netherlands.Methods and resultsWe used the MyDiagnostick to screen for AF in persons who participated in influenza vaccination sessions of ten Dutch primary care practices. In case of suspected AF detection by the stick, the recorded 1-min ECG registrations were analysed by a cardiologist. We scrutinized electronic medical files of the general practitioners to obtain information about the cases screened. Multivariable logistic regression analysis was performed to predict the relation between patient characteristics and a new screen-detected diagnosis of AF. In total, 3269 persons were screened for AF during the influenza vaccination sessions of 10 general practitioner practices. As a result, 37 (1.1%) new cases of AF were detected. Prior transient ischeamic attack or stroke (OR 6.05; 95%CI 1.93–19.0), and age (OR 1.09 per year; 95% CI 1.05–1.14) were independent predictors for such newly screen-detected AF. Of the 37 screen-detected AF cases, 2.7% had a CHA2DS2-VASc of 0, 18.9% a score of 1, and 78.4% a score of 2 or more. The majority needed oral anticoagulant therapy.ConclusionsScreening seems feasible with an easy to use single-lead, hand-held ECG device with automatic AF detection during influenza vaccination in primary care and results in a ‘1-day’ yield of 1.1% new cases of AF.Trial registration clinicaltrials.govNCT02006524.

Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine

BackgroundAtrial fibrillation (AF) is the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Automatic detection of AF could substantially help in early diagnosis, management and consequently prevention of the complications associated with chronic AF. In this paper, we propose a novel method for automatic AF detection. MethodStationary wavelet transform and support vector machine have been employed to detect AF episodes. The proposed method eliminates the need for P-peak or R-Peak detection (a pre-processing step required by many existing algorithms), and hence its performance (sensitivity, specificity) does not depend on the performance of beat detection. The proposed method has been compared with those of the existing methods in terms of various measures including performance, transition time (detection delay associated with transitioning from a non-AF to AF episode), and computation time (using MIT-BIH Atrial Fibrillation database). ResultsResults of a stratified 2-fold cross-validation reveals that the area under the Receiver Operative Characteristics (ROC) curve of the proposed method is 99.5%. Moreover, the method maintains its high accuracy regardless of the choice of the parameters׳ values and even for data segments as short as 10s. Using the optimal values of the parameters, the method achieves sensitivity and specificity of 97.0% and 97.1%, respectively. DiscussionThe proposed AF detection method has high sensitivity and specificity, and holds several interesting properties which make it a suitable choice for practical applications.

Integration of cardiac monitoring with video to enable service transformation

The future of digital care will be significantly enhanced by the integration and interoperation of systems and services. Collaboration between clinicians and service providers will allow combined services to be shaped such that the whole is greater than the sum of the parts. In future services need to enable people to continue with their lives whilst being supported, rapidly and appropriately when required. We present the results of a project to use an innovative cardiac event loop recorder (ELR) and screening service in cooperation with the use of video consultations. The ELR service detects and records abnormal ECG traces. The connected consultant can choose to receive a PDF of any abnormal trace within seconds or have the trace pre-screened by a cardiac physiologist. The consultant can then initiate a video call to the person and discuss the event. The patient might then attend a consultation in person. The goal is to significantly improve the detection of abnormal events and offer immediacy of support and diagnosis i.e. quality improvement by early diagnosis and potential cost saving in the immediate term by reducing surveillance appointments and in the long term by reducing burden of disease e.g. stroke reduction by earlier AF detection This approach facilitates two modes of care delivery that are innovative: The first is the capture of infrequent events. An example case involved a 66-year-old hypertensive female who suffered from 2 years history of palpitations and had been treated for anxiety and ventricular ectopic beats. Physical examination was normal. Echocardiography showed structurally normal heart. Her electrocardiogram showed normal sinus rhythm with normal PR and QT segments Since several 24 hours Holter monitoring episodes had proved to be unsuccessful in the past it was decided to monitor the patient with the smart event ELR. The patient was equipped with a one-channel smart ELR that was used over a period of 2 weeks of continuous monitoring. The recorder was programmed to automatically send both manually triggered cardiac events as well as automatically triggered events for atrial fibrillation (AF). For the latter, the automatic AF detection algorithm had been activated. All events were automatically transferred using GPRS (tele-ECG option) and in virtually real time. International Digital Health and Care Congress, The King’s Fund, London, September 10-12 2014. International Journal of Integrated Care – Volume 14, 01 November – URN:NBN:NL:UI:10-1-116508– http://www.ijic.org/ Over the course of the next days the recorder sent in ECGs, which showed clear evidence of paroxysmal atrial fibrillation (PAF) as a cause of her intermittent palpitation. During these episodes, she felt unwell with erratic heart rate and light-headedness. The episodes lasted between 30 seconds and up to 2 minutes, confirming the diagnosis of PAF. For patients with long-term conditions that include cardiovascular disease the approach offers a pro-active and supportive way to manage people in their own homes. Both approaches address several of the NHS Outcomes Framework domains, specifically: • reducing premature mortality. • ensuring people feel supported to manage their condition. • reducing the amount of time spent in hospital. • improving people’s experience of outpatient care. • improving hospital’s responsiveness to patient’s needs. During the presentation there will be a live demonstration of the cardiac monitoring and the video service.

A Novel Method for Real‐Time Atrial Fibrillation Detection in Electrocardiograms Using Multiple Parameters

Automatic detection of atrial fibrillation (AF) in electrocardiograms (ECGs) is beneficial for AF diagnosis, therapy, and management. In this article, a novel method of AF detection is introduced. Most current methods only utilize the RR interval as a critical parameter to detect AF; thus, these methods commonly confuse AF with other arrhythmias. We used the average number of f waves in a TQ interval as a characteristic parameter in our robust, real-time AF detection method. Three types of clinical ECG data, including ECGs from normal, AF, and non-AF arrhythmia subjects, were downloaded from multiple open access databases to validate the proposed method. The experimental results suggested that the method could distinguish between AF and normal ECGs with accuracy, sensitivity, and positive predictive values (PPVs) of 93.67%, 94.13%, and 98.69%, respectively. These values are comparable to those of related methods. The method was also able to distinguish between AF and non-AF arrhythmias and had performance indexes (accuracy 94.62%, sensitivity 94.13%, and PPVs 97.67%) that were considerably better than those of other methods. Our proposed method has prospects as a practical tool enabling clinical diagnosis, treatment, and monitoring of AF.

Time-Varying Coherence Function for Atrial Fibrillation Detection

We introduce a novel method for the automatic detection of atrial fibrillation (AF) using time-varying coherence functions (TVCF). The TVCF is estimated by the multiplication of two time-varying transfer functions (TVTFs). The two TVTFs are obtained using two adjacent data segments with one data segment as the input signal and the other data segment as the output to produce the first TVTF; the second TVTF is produced by reversing the input and output signals. We found that the resultant TVCF between two adjacent normal sinus rhythm (NSR) segments shows high coherence values (near 1) throughout the entire frequency range. However, if either or both segments partially or fully contain AF, the resultant TVCF is significantly lower than 1. When TVCF was combined with Shannon entropy (SE), we obtained even more accurate AF detection rate of 97.9% for the MIT-BIH atrial fibrillation (AF) database (n = 23) with 128 beat segments. The detection algorithm was tested on four databases using 128 beat segments: the MIT-BIH AF database, the MIT-BIH NSR database ( n = 18), the MIT-BIH Arrhythmia database ( n = 48), and a clinical 24-h Holter AF database ( n = 25). Using the receiver operating characteristic curves from the combination of TVCF and SE, we obtained a sensitivity of 98.2% and specificity of 97.7% for the MIT-BIH AF database. For the MIT-BIH NSR database, we found a specificity of 99.7%. For the MIT-BIH Arrhythmia database, the sensitivity and specificity were 91.1% and 89.7%, respectively. For the clinical database (24-h Holter data), the sensitivity and specificity were 92.3% and 93.6%, respectively. We also found that a short segment (12 beats) also provided accurate AF detection for all databases: sensitivity of 94.7% and specificity of 90.4% for the MIT-BIH AF, specificity of 94.4% for the MIT-BIH-NSR, the sensitivity of 92.4% and specificity of 84.1% for the MIT-BIH arrhythmia, and sensitivity of 93.9% and specificity of 84.4% for the clinical database. The advantage of using a short segment is more accurate AF burden calculation as the timing of transitions between NSR and AF are more accurately detected.

Automatic Detection of Atrial Fibrillation Using Empirical Mode Decomposition and Statistical Approach

Automatic detection of different cardiac abnormalities is an emerging field of study in assistive diagnosis technology for ca rdiac diseases. Atrial fibrillation (AF) is a kind of arrhythmia which increases risk of heart attack especially to the older people. Detection of AF at the early stage may cause prevention of serious stoke. This paper presents a method of automatic detection of AF by using higher order statistical moments of ECG signal in Empirical Mode Decomposition (EMD) domain. The proposed technique operates in two stages. First stage consists of decomposition of denoised ECG into intrinsic mode functions (IMF) and derives the statistical parameters like variance and standard deviation for classification from each IMF. In the second s tage these features are used by a supervised classifier to distinguish between normal and AF ECG rhythms. The performance of this method is tested with the MIT-BIH arrhythmia data base. Possibility to use different IMFs is also tested and 96% sensitivity is achieved in IMF 4.

Improvements in Automatic Detection of Atrial Fibrillation for Holter Monitoring

Improvements in Automatic Detection of Atrial Fibrillation for Holter Monitoring

Automatic Detection of Atrial Fibrillation in Cardiac Vibration Signals

We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.

High accuracy in automatic detection of atrial fibrillation for Holter monitoring

Atrial fibrillation (AF) has been considered as a growing epidemiological problem in the world, with a substantial impact on morbidity and mortality. Ambulatory electrocardiography (e.g., Holter) monitoring is commonly used for AF diagnosis and therapy and the automated detection of AF is of great significance due to the vast amount of information provided. This study presents a combined method to achieve high accuracy in AF detection. Firstly, we detected the suspected transitions between AF and sinus rhythm using the delta RR interval distribution difference curve, which were then classified by a combination analysis of P wave and RR interval. The MIT-BIH AF database was used for algorithm validation and a high sensitivity and a high specificity (98.2% and 97.5%, respectively) were achieved. Further, we developed a dataset of 24-h paroxysmal AF Holter recordings (n=45) to evaluate the performance in clinical practice, which yielded satisfactory accuracy (sensitivity=96.3%, specificity=96.8%).

Performance of an implantable automatic atrial fibrillation detection device: impact of software adjustments and relevance of manual episode analysis

AimsImplantable loop recorders (ILRs) with specific atrial fibrillation (AF) detection algorithms (ILR-AF) have been developed for continuous AF monitoring. We sought to analyse the clinical value of a new AF monitoring device and to compare it to serial 7-day Holter.Methods and resultsSixty-four consecutive patients suffering from paroxysmal AF were included in this prospective analysis and received an ILR-AF. Manual electrogram analysis was performed for each automatically detected episode and each was categorized into one of three possible diagnoses: ‘no AF', ‘definite AF', and ‘possible AF' (non-diagnostic). Analysis was performed separately before and after a software upgrade that was introduced during the course of the study. A subgroup of patients (51 of 64) underwent AF catheter ablation with subsequent serial 7-day Holter in comparison with the ILR-AF. A total of 333 interrogations were performed (203 before and 130 after software upgrade). The number of patients with AF misdetection was significantly reduced from 72 to 44% following the software upgrade (P= 0.001). The number of patients with non-diagnostic interrogations went from 38 to 16% (P= 0.001). Compared with serial 7-day Holter, the ILR-AF had a tendency to detect a higher number of patients with AF recurrences (31 vs. 24%; P = 0.125).ConclusionsThe rate of AF detection on ILR-AF may be higher compared with standard AF monitoring. However, false-positive AF recordings hamper the clinical value. Developements in device technology and device handling are necessary to minimize non-diagnostic interrogations.

A Novel Method for Detection of the Transition Between Atrial Fibrillation and Sinus Rhythm

Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study, we proposed a novel method for detection of the transition between AF and sinus rhythm based on RR intervals. First, we obtained the delta RR interval distribution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps were used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH normal sinus rhythm (NSR) database) were used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test, we have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.

Towards automatic detection of atrial fibrillation: A hybrid computational approach

In this study, new methods coupling genetic programming with orthogonal least squares (GP/OLS) and simulated annealing (GP/SA) were applied to the detection of atrial fibrillation (AF) episodes. Empirical equations were obtained to classify the samples of AF and Normal episodes based on the analysis of RR interval signals. Another important contribution of this paper was to identify the effective time domain features of heart rate variability (HRV) signals via an improved forward floating selection analysis. The models were developed using the MIT–BIH arrhythmia database. A radial basis function (RBF) neural networks-based model was further developed using the same features and data sets to benchmark the GP/OLS and GP/SA models. The diagnostic performance of the GP/OLS and GP/SA classifiers was evaluated using receiver operating characteristics analysis. The results indicate a high level of efficacy of the GP/OLS model with sensitivity, specificity, positive predictivity, and accuracy rates of 99.11%, 98.91%, 98.23%, and 99.02%, respectively. These rates are equal to 99.11%, 97.83%, 98.23%, and 98.534% for the GP/SA model. The proposed GP/OLS and GP/SA models have a significantly better performance than the RBF and several models found in the literature.

Automatic Real Time Detection of Atrial Fibrillation

Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with significant morbidity and mortality. Timely diagnosis of the arrhythmia, particularly transient episodes, can be difficult since patients may be asymptomatic. In this study, we describe a robust algorithm for automatic detection of AF based on the randomness, variability and complexity of the heart beat interval (RR) time series. Specifically, we employ a new statistic, the Turning Points Ratio, in combination with the Root Mean Square of Successive RR Differences and Shannon Entropy to characterize this arrhythmia. The detection algorithm was tested on two databases, namely the MIT-BIH Atrial Fibrillation Database and the MIT-BIH Arrhythmia Database. These databases contain several long RR interval series from a multitude of patients with and without AF and some of the data contain various forms of ectopic beats. Using thresholds and data segment lengths determined by Receiver Operating Characteristic (ROC) curves we achieved a high sensitivity and specificity (94.4% and 95.1%, respectively, for the MIT-BIH Atrial Fibrillation Database). The algorithm performed well even when tested against AF mixed with several other potentially confounding arrhythmias in the MIT-BIH Arrhythmia Database (Sensitivity = 90.2%, Specificity = 91.2%).

Abstract 1737: A Strategy for Continuous ECG Recording in Patients Post Pulmonary Vein Isolation for Atrial Fibrillation

Introduction : Following pulmonary vein isolation (PVI) for management of atrial fibrillation (AF), may centers obtain continuous ECG recordings for several weeks in order to detect early recurrences of AF. However, the implications of early AF following PVI in an individual patient are unknown as is the optimal duration of monitoring in these patients. Methods : We evaluated 72 pts (60 ± 11 yrs, 67% male, 67% paroxysmal AF) who underwent PVI and were followed for >= 6 months. At hospital discharge, all pts were fitted with an external event loop recorder (LifeWatch AF Express) for 14 weeks for the continuous automatic detection of AF (defined as an episode lasting >= 30 sec). Clinical follow-up occurred at 1, 3 and 6 months post-PVI; procedural success was defined by freedom from AF at the 6 month follow-up. Results : During the 14-week loop monitoring period, no AF was observed in 25 (35%) patients. Only 2 of these pts subsequently developed AF; in both cases, AF occurred 3– 6 months post-PVI. In contrast, 47 (65%) patients had at least 1 AF episode, most commonly (39/47 pts, 83%) within the first 2 weeks of PVI. In fact, absence of AF during the first 2-weeks of loop monitoring identified a cohort of patients with high likelihood of procedural success at 6 months (Figure ). Conclusions : Our data support a rationale for at least 2-weeks of continuous automatic loop ECG monitoring in all pts post-PVI. Patients without AF during this period can be expected to have excellent long-term outcome. Longer periods of ECG monitoring may be better limited to patients in whom AF is observed during initial 2-week monitoring.

797 Clinical validation of the AF-ALARM, a dedicated loop recorder for automatic detection of atrial fibrillation

797 Clinical validation of the AF-ALARM, a dedicated loop recorder for automatic detection of atrial fibrillation

797 Clinical validation of the AF-ALARM, a dedicated loop recorder for automatic detection of atrial fibrillation

797 Clinical validation of the AF-ALARM, a dedicated loop recorder for automatic detection of atrial fibrillation

Clinical validation of the AF-alarm, a dedicated loop recorder for automatic detection of atrial fibrillation

Background: In order to enable proper determination of AF-burden and asymptomatic arrhythmia episodes in patients with paroxysmal AF, we developed the AF-ALARM. This is an easy wearable, external loop recorder with buit-in algorithm for automated detection and electrocardiogram storage of AF, based on RR-irregularity during two-minute intervals. Previously, the algorithm proved to be very accurate in detecting AF on RR-interval files from the MIT database or derived from Holter recordings.

Detection of atrial fibrillation in patients with dual chamber pacemakers: Holter recordings versus stored electrograms

Background: Accuracy of atrial fibrillation (AF) detection in Holter recordings of patients (pts) with pacemakers is not clear. Modern devices offer automatic AF detection and electrogram (EGM) storage during AF episodes. In a substudy of BEATS (Balanced Evaluation of Atrial Tachyarrhythmias in Stimulated Patients), the correlation between Holter and stored EGMs was assessed.

Automatic detection of atrial fibrillation and flutter by wave rectification method

Rapid detection of atrial flutter or fibrillation is needed in intensive care or home ECG monitoring with alarm generation, and in portable monitors with warning function, etc. Detection and assessment of these atrial abnormalities is necessary in computerized morphological analysis as well, to decide whether parameter measurements should be rejected, restricted to QRS and/or T wave only, or limited to those leads where atrial flutter or fibrillation waves are less expressed. A method for the detection and measurement of atrial flutter and fibrillation in the T-P segments of the ECG is proposed. An atrial flutter/ fibrillation parameter (AFF) is defined as the mean value of the differentiated filtered and rectified signal in these segments. The AFF has been measured in 329 patients from an annotated atrial flutter-fibrillation database. A threshold of AFF=0.35% with respect to the maximum signal excursion was chosen by a heuristic algorithm, to separate patients with atrial arrhythmia. The accuracy of the method was 91.8%. The positive and negative detection errors of the AFF classification are discussed.