Automatic Detection Of Atrial Fibrillation Research Articles

Identification of atrial fibrillation using descriptive statistic features and adaptive Neuro-Fuzzy inference system

Atrial fibrillation (AF) is one of the most common arrhythmia which can cause a serious problem. Nevertheless, well treated AF might not lead to any further complication. Early detection of AF could be an important preventative step that have to be conducted. In this article, we aim to make an automatic detection of atrial fibrillation. Seven descriptive statistic features have been utilized to detect AF. The features obtained could differ between two condition: normal and AF. Later, we use Adaptive Neuro-Fuzzy Inference System (ANFIS) as a classification method. Sugeno-type fuzzy inference system along with Gaussian type of fuzzy are utilized to classify the condition. The proposed method is applied on MIT-BIH Atrial Fibrillation database. The performance obtained from this proposed method might be considered for a medical application.

Continuous heart rate monitoring for automatic detection of atrial fibrillation with novel bio-sensing technology

BackgroundAsymptomatic atrial fibrillation [AF] is an important cause of fatal or disabling stroke. A continuous heart-rate monitoring device, comfortable enough to be worn continuously and reliable enough to detect AF, would allow for prompt initiation of anticoagulation therapy to prevent stroke. MethodsWe studied a new custom-made wearable photo-plethysmograph [PPG] wrist-watch sensor, specifically designed for continuous heart rate monitoring and incorporating contact and motion noise-filters. We tested its ability to automatically detect AF in patients undergoing elective cardioversion of AF, using simultaneously recorded electrocardiogram [ECG] as gold standard. ResultsA total of 18,608 consecutive R-R-interval measurements were recorded simultaneously with PPG and ECG in 20 patients, including 12,521 [67.3%] R-R-intervals during AF and 6087 [32.7%] R-R intervals during sinus rhythm. Scatter plots and Bland-Altman plots demonstrated that the PPG signals were highly correlated to the simultaneously recorded ECG [R = 0.980, p < 0.001], both during AF and during sinus rhythm. The automatic algorithm distinguished AF from sinus rhythm with a sensitivity of 100% and specificity of 93.1%. ConclusionsThis PPG-based wrist-watch sensor reliably detected AF in non-ambulatory patients.

Correction: Billeci, L.; et al. Automatic Detection of Atrial Fibrillation and Other Arrhythmias in ECG Recordings Acquired by a Smartphone Device. Electronics 2018, 7, 199

The authors wish to make the following corrections to our published paper [1].[...]

Automatic Detection of Atrial Fibrillation and Other Arrhythmias in ECG Recordings Acquired by a Smartphone Device

Atrial fibrillation (AF) is the most common cardiac disease and is associated with other cardiac complications. Few attempts have been made for discriminating AF from other arrhythmias and noise. The aim of this study is to present a novel approach for such a classification in short ECG recordings acquired using a smartphone device. The implemented algorithm was tested on the Physionet Computing in Cardiology Challenge 2017 Database and, for the purpose of comparison, on the MIT-BH AF database. After feature extraction, the stepwise linear discriminant analysis for feature selection was used. The Least Square Support Vector Machine classifier was trained and cross-validated on the available dataset of the Challenge 2017. The best performance was obtained with a total of 30 features. The algorithm produced the following performance: F1 Normal rhythm = 0.92; F1 AF rhythm: 0.82; F1 Other rhythm = 0.75; Global F1 = 0.83, obtaining the third best result in the follow-up phase of the Physionet Challenge. On the MIT-BH ADF database the algorithm gave the following performance: F1 Normal rhythm = 0.98; F1 AF rhythm: 0.99; Global F1 = 0.98. Since the algorithm reliably detect AF and other rhythms in smartphone ECG recordings, it could be applied for personal health monitoring systems.

ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network

Objective: The electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the most common cardiac rhythm disturbance and affects ~2% of the general population in industrialized countries. Automatic AF detection in clinics remains a challenging task due to the high inter-patient variability of ECGs, and unsatisfactory existing approaches for AF diagnosis (e.g. atrial or ventricular activity-based analyses). Approach: We have developed RhythmNet, a 21-layer 1D convolutional recurrent neural network, trained using 8528 single-lead ECG recordings from the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge, to classify ECGs of different rhythms including AF automatically. Our RhythmNet architecture contained 16 convolutions to extract features directly from raw ECG waveforms, followed by three recurrent layers to process ECGs of varying lengths and to detect arrhythmia events in long recordings. Large 15 × 1 convolutional filters were used to effectively learn the detailed variations of the signal within small time-frames such as the P-waves and QRS complexes. We employed residual connections throughout RhythmNet, along with batch-normalization and rectified linear activation units to improve convergence during training. Main results: We evaluated our algorithm on 3658 testing data and obtained an F1 accuracy of 82% for classifying sinus rhythm, AF, and other arrhythmias. RhythmNet was also ranked 5th in the 2017 CinC Challenge. Significance: Potentially, our approach could aid AF diagnosis in clinics and be used for patient self-monitoring to improve the early detection and effective treatment of AF.

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks.

Atrial fibrillation (AF) is the most common cardiac arrhythmias causing morbidity and mortality. AF may appear as episodes of very short (i.e., proximal AF) or sustained duration (i.e., persistent AF), either form of which causes irregular ventricular excitations that affect the global function of the heart. It is an unmet challenge for early and automatic detection of AF, limiting efficient treatment strategies for AF. In this study, we developed a new method based on continuous wavelet transform and 2D convolutional neural networks (CNNs) to detect AF episodes. The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities. Then a 2D CNN was trained to improve AF detection performance. The MIT-BIH Atrial Fibrillation Database was used for evaluating the algorithm. The efficacy of the proposed method was compared with those of some existing methods, most of which implemented the same dataset. The newly developed algorithm using CNNs achieved 99.41, 98.91, 99.39, and 99.23% for the sensitivity, specificity, positive predictive value, and overall accuracy (ACC) respectively. As the proposed algorithm targets the time-frequency feature of ECG signals rather than isolated atrial or ventricular activity, it has the ability to detect AF episodes for using just five beats, suggesting practical applications in the future.

A deep learning approach for real-time detection of atrial fibrillation

Goal: To develop a robust and real-time approach for automatic detection of atrial fibrillation (AF) in long-term electrocardiogram (ECG) recordings using deep learning (DL). Method: An end-to-end model combining the Convolutional- and Recurrent-Neural Networks (CNN and RNN) was proposed to extract high level features from segments of RR intervals (RRIs) in order to classify them as AF or normal sinus rhythm (NSR). Results: The model was trained and validated on three different databases including a total of 89 subjects. It achieved a sensitivity and specificity of 98.98% and 96.95%, respectively, validated through a 5-fold cross-validation. Additionally, the proposed model was found to be computationally efficient and it was capable of analyzing 24 h of ECG recordings in less than one second. The proposed algorithm was also tested on the unseen datasets to examine its robustness in detecting AF for new recordings which resulted in 98.96% and 86.04% for specificity and sensitivity, respectively. Conclusion: Compared to the state-of-the-art models evaluated on standard benchmark ECG datasets, the proposed model produced better performance in detecting AF. Additionally, since the model learns features directly from the data, it avoids the need for clever/cumbersome feature engineering.

P1937The MyrhythmTM app for automatic detection of atrial fibrillation using commercial chest strap monitors: proof-of-concept accuracy testing in patients undergoing external electrical cardioversion

P1937The MyrhythmTM app for automatic detection of atrial fibrillation using commercial chest strap monitors: proof-of-concept accuracy testing in patients undergoing external electrical cardioversion

Abductive reasoning as a basis to reproduce expert criteria in ECG atrial fibrillation identification

Objective: This work aims at providing a new method for the automatic detection of atrial fibrillation, other arrhythmia and noise on short single-lead ECG signals, emphasizing the importance of the interpretability of the classification results. Approach: A morphological and rhythm description of the cardiac behavior is obtained by a knowledge-based interpretation of the signal using the Construe abductive framework. Then, a set of meaningful features are extracted for each individual heartbeat and as a summary of the full record. The feature distributions can be used to elucidate the expert criteria underlying the labeling of the 2017 PhysioNet/CinC Challenge dataset, enabling a manual partial relabeling to improve the consistency of the training set. Finally, a tree gradient boosting model and a recurrent neural network are combined using the stacking technique to provide an answer on the basis of the feature values. Main results: The proposal was independently validated against the hidden dataset of the Challenge, achieving a combined F1 score of 0.83 and tying for the first place in the official stage of the Challenge. This result was even improved in the follow-up stage to 0.85 with a significant simplification of the model, attaining the highest score so far reported on the hidden dataset. Significance: The obtained results demonstrate the potential of Construe to provide robust and valuable descriptions of temporal data, even with the presence of significant amounts of noise. Furthermore, the importance of consistent classification criteria in manually labeled training datasets is emphasized, and the fundamental advantages of knowledge-based approaches to formalize and validate those criteria are discussed.

Atrial Fibrillation Detection in Short Single Lead ECG Recordings Using Wavelet Transform and Artificial Neural Networks.

Atrial fibrillation (AF) is a common health issue, not only in developed countries but also in developing ones. AF can lead to strokes, heart failures, and even death if it is not diagnosed and treated on time, therefore automatic detection of AF is an urgent need, particularly using Internet- connected devices that can alert healthcare services. Detection of AF typically involves the analysis of electrocardiogram (ECG) recordings, where P-waves that characterize the atrial activity are substituted with f-waves of variable amplitude and duration. In this paper, we used the discrete wavelet transform to decompose the ECG signal into detail and approximation coefficients with different time-frequency resolutions. Features extracted from ECG signals, RR interval time series and detail and approximation coefficients were used as inputs to an artificial neural network trained to identify four classes of heart rhythms: normal sinus rhythm (NSR), AF, other rhythms (OR) and noisy signals (NS). By performing a Monte Carlo 10- fold cross-validation of 10 iterations approach, average micro F1 scores of 83.64%, 61.61%, 56.88% and 53.88% to classify NSR, AF, OR and NS respectively, and average macro F1 of 64.00% were obtained on the publicly available training set of PhysioNet/Computing in Cardiology Challenge 2017. In addition, in a one-vs.-the-rest strategy, i.e., AF-vs-the-rest, averages sensitivity and specificity of 95.70% and 72.39% respectively were achieved to classify AF recordings.

Effectiveness and accuracy of arrhythmia detection algorithm with the Reveal LINQ insertable cardiac monitor

Objective: To investigate the accuracy of arrhythmia detection algorithm and the effectiveness of Reveal LINQ insertable cardiac monitor (ICM). Methods: This single-center, descriptive, non-controlled clinical study was designed to enroll consecutive patients who were implanted with Reveal LINQ ICM in Minneapolis Veterans Affairs Medical Center from June 19, 2014 to April 18, 2017. The safety of implant procedure, the accuracy of automatic arrhythmia detection algorithm and the clinical effectiveness were evaluated and validated independently by two physicians. Results: Sixty patients with average age of (65±13) years (30-89 years) were enrolled and the follow-up duration was 466 days (30-1 072 days). Of these, 57 patients are male. The indications of ICM implantation were unexplained syncope in 35 cases, cryptogenic stroke in 13 cases, atrial fibrillation (AF) management in 5 cases, and palpitations in 7 cases. The implantation procedure was safe and successful in all patients without complications. The detection accuracy of arrhythmia algorithm was 86% (620/723), 82% (2 369/2 876), and 30% (411/1 380) for tachycardia, bradycardia, and pause events, respectively. The detection accuracy of arrhythmia algorithm for AF was 17% (5/30), 60% (1 569/2 632) and 69% (172/251) for cryptogenic stroke, unexplained syncope, and AF management, respectively. Important clinical findings were observed in 15% (9/60) patients. Only 5% (3/60) patients had important findings based on the intended clinical indications. Conclusion: The Reveal LINQ ICM is a safe, simple, and effective procedure for the detection of tachycardia and bradycardia events. However, the automatic AF and pause events detection algorithm had high false positive rate which requires further improvement.

Detecting atrial fibrillation from short single lead ECGs using statistical and morphological features

Objective: Point of care ECG devices can improve the early detection of atrial fibrillation (AF). The efficiency of such devices depends on the capability of automatic AF detection against normal sinus rhythm and other arrhythmias from a short single lead ECG record in the presence of noise and artifacts. The objective of this study was to develop an algorithm that classifies a short single lead ECG record into ‘Normal’, ‘AF’, ‘Other’ and ‘Noisy’ classes, and identify the challenges in developing such algorithms and potential mitigation steps. Approach: Rule-based identification was used to detect lead inversion and records too noisy to be of immediate use. A set of statistical and morphological features describing the rhythm was then extracted, and support vector machine classifiers were used to classify records into three classes: ‘Normal’, ‘AF’ or ‘Other’. The algorithm was trained and tested using 12 186 short single lead ECGs recorded on a point of care device made available via the Computing in Cardiology Challenge 2017. Main results: The algorithm achieved a sensitivity of 77.5%, a specificity of 97.9% and an accuracy of 96.1% in the detection of AF from a non-AF rhythm in a five-fold cross validation. It achieved F1 measures of 89%, 78% and 67% for ‘Normal’, ‘AF’ and ‘Other’ classes, respectively, when evaluated with a hidden test set. The overall challenge score was 78%. Significance: Most existing algorithms can distinguish the AF rhythm from the normal sinus rhythm when ECG recordings are clean and are obtained with multi-lead systems, while their ability to discriminate against other arrhythmias and noise remains largely unknown. This study proposes an algorithm that classifies a short single lead ECG record from point of care devices into ‘Normal’, ‘AF’, ‘Other’ and ‘Noisy’ classes and discusses computational approaches to mitigate any unique challenges such as lead inversion, low amplitude signals, noise and artifacts.

Automatic Atrial Fibrillation Detection Based on Heart Rate Variability and Spectral Features

Atrial fibrillation (AF) is one of the most common sustained arrhythmias, affecting about 1% of the population around the world. Rapid popularization of portable and wearable devices in recent years makes widespread personalized and mobile healthcare get closer to reality than ever before. This paper presents a method aiming for automatic detection of AF from short single lead electrocardiogram (ECG) recordings. Since AF is a kind of arrhythmia being likely to alter the dynamics of heart rhythms and/or the morphological characteristics in ECG tracings, heart rate variability (HRV)-based metrics and frequency analysis are adopted as feature extractors. We validate our method on a public available data set comprised of short ECG recordings of normal rhythm (N), AF (A), and other arrhythmias (O) by support vector machine and bagging trees. For two-class classification problems (N versus A), accuracy varies from 92.0% to 96.6% under different additional noise levels. For three-class classification problem (N versus A versus O), accuracy as high as 82.0% is obtained. Experimental results suggest than even for a relatively short ECG recording, nonlinear descriptors of HRV are still efficient and robust for AF detection.

A novel approach for automatic detection of Atrial Fibrillation based on Inter Beat Intervals and Support Vector Machine.

Atrial fibrillation (AF) is the most common cardiac arrhythmia associated with a major economic burden for the society. Automatic detection of AF in long term recordings can efficiently assist in early diagnosis and management of comorbidities associated with AF. This study presents a novel approach for AF detection based on Inter Beat Intervals (IBI) extracted from long term electrocardiogram (ECG) recordings. Five time-domain features are extracted from the IBIs and a Support Vector Machine (SVM) is used for classification. The results are compared to a state of the art algorithm based on raw ECG. Both algorithms are evaluated on the MIT-BIH Atrial Fibrillation database resulting in equally high classification performance (Sensitivity ≥ 95%). The proposed approach requires detection of R-peaks in the ECG signal but allows for significantly reduced computation time without loss of performance.

Automatic Atrial Fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability.

Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classification. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed to evaluate the performance of the proposed method. Results from these extensive studies showed very promising results, with an average sensitivity of 96.51%, a specificity of 99.19%, and an overall accuracy of 98.22%.

Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation.

Atrial fibrillation (AF) monitoring and diagnosis require automatic AF detection methods. In this paper, a novel image-based AF detection method was proposed. The map was constructed by plotting changes of RR intervals (△RR) into grid panes. First, the map was divided into grid panes with 20ms fixed resolution in y-axes and 15-60s step length in x-axes. Next, the blank pane ratio (BPR), the entropy and the probability density distribution were processed using linear support-vector machine (LSVM) to classify AF and non-AF episodes. The performance was evaluated based on four public physiological databases. The Cohen's Kappa coefficients were 0.87, 0.91 and 0.64 at 50s step length for the long-term AF database, the MIT-BIH AF database and the MIT-BIH arrhythmia database, respectively. Best results were achieved as follows: (1) an accuracy of 93.7%, a sensitivity of 95.1%, a specificity of 92.0% and a positive predictive value (PPV) of 93.5% were obtained for the long-term AF database at 60s step length. (2) An accuracy of 95.9%, a sensitivity of 95.3%, a specificity of 96.3% and a PPV of 94.1% were obtained for the MIT-BIH AF database at 40s step length. (3) An accuracy of 90.6%, a sensitivity of 94.5%, a specificity of 90.0% and a PPV of 55.0% were achieved for the MIT-BIH arrhythmia database at 60s step length. (4) Both accuracy and specificity were 96.0% for the MIT-BIH normal sinus rhythm database at 40s step length. In conclusion, the intuitive grid map of delta RR intervals offers a new approach to achieving comparable performance with previously published AF detection methods.

Atrial fibrillation detected by external loop recording for seven days or two-day simultaneous Holter recording: A comparison in patients with ischemic stroke or transient ischemic attack

Atrial fibrillation (AF) is the most common cardiac cause of ischemic stroke and transient ischemic attack (IS/TIA). AimTo compare the diagnostic value of seven-day external loop recording (ELR) and two-day Holter recording for detecting AF after IS/TIA. Methods191 IS/TIA patients without AF history. Endpoint was AF >30s. We started two-day Holter recording and seven-day ELR simultaneously. ResultsSeven-day ELR and two-day Holter recording detected the same three AF patients. ELR detected another six patients with AF adjudicated by cardiologists, four detections after Holter (3 vs. 7, p=0.125) and two false-positive detections during Holter. Seven-day ELR automatically classified 50/191 patients (26%) with AF, but only 7/50 (14%) were confirmed as AF by cardiologists. ConclusionSeven-day ELR did not detect significantly more patients with AF than two-day Holter recording. 86% of patients with ELR-classified AF were false positives, indicating a poor performance of the automatic AF detection algorithm used.

Automatic Detection of Congestive Heart Failure and Atrial Fibrillation with Short RR Interval Time Series

Atrial fibrillation (AF) and Congestive heart failure (CHF) are increasingly widespread, costly, deadly diseases and are associated with significant morbidity and mortality. In this study, we analyzed three statistical methods for automatic detection of AF and CHF based on the randomness, variability and complexity of the heart beat interval, which is RRI time series. Specifically, we used short RRI time series with 16 beats and employed the normalized root mean square of successive RR differences (RMSSD), the sample entropy and the Shannon entropy. The detection performance was analyzed using four large well documented databases, namely the MIT-BIH Atrial fibrillation (n=23), the MIT-BIH Normal Sinus Rhythm (n=18), the BIDMC Congestive Heart Failure (n=13) and the Congestive Heart Failure RRI databases (n=25). Using thresholds by Receiver Operating Characteristic (ROC) curves, we found that the normalized RMSSD provided the highest accuracy. The overall sensitivity, specificity and accuracy for AF and CHF were 0.8649, 0.9331 and 0.9104, respectively. Regarding CHF detection, the detection rate of CHF (NYHA III-IV) was 0.9113 while CHF (NYHA I-II) was 0.7312, which shows that the detection rate of CHF with higher severity is higher than that of CHF with lower severity. For the clinical 24 hour data (n=42), the overall sensitivity, specificity and accuracy for AF and CHF were 0.8809, 0.9406 and 0.9108, respectively, using normalized RMSSD.

Current and Emerging Uses of Insertable Cardiac Monitors: Evaluation of Syncope and Monitoring for Atrial Fibrillation.

Insertable cardiac monitors (ICMs) have provided clinicians with a superb tool for assessing infrequent or potentially asymptomatic arrhythmias. ICMs have shown their usefulness in the evaluation of unexplained syncope, providing high diagnostic yields in a cost-effective manner. While unexplained syncope continues to be the most common reason for their use, ICMs are increasingly being used for the monitoring of atrial fibrillation (AF). Recent trials have demonstrated that a substantial proportion of patients with cryptogenic stroke have AF detected only by the prolonged monitoring provided by ICMs. A particularly promising and emerging use for ICMs is in the management of anticoagulation in patients with known paroxysmal AF. The introduction in recent years of ICMs with automatic AF detection algorithms and continuous remote monitoring in combination with novel oral anticoagulants have opened the door for targeted anticoagulation guided by remote monitoring, a strategy that has recently shown promise in pilot studies of this technique. While further research is needed before official recommendations can be given, this use of ICMs opens exciting new possibilities for personalized medicine that could potentially reduce bleeding risk and improve quality of life in patients with atrial fibrillation.

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.