Performance Of Atrial Fibrillation Detection Research Articles

Atrial fibrillation detection on reconstructed photoplethysmography signals collected from a smartwatch using a denoising autoencoder

Photoplethysmography (PPG) signals collected by wearables have been shown to be effective in accurate detection of atrial fibrillation (AF), provided that the data are devoid of motion and noise artifacts (MNA). Many studies have been previously conducted to detect AF arrhythmia using PPG data; however, the subjects were mostly in clinics or controlled settings with data collection lasting several minutes to at most several hours with minimal MNA. Our study, Pulsewatch, differs from previous AF studies in that PPG data from smartwatches prescribed to stroke survivors were continuously collected for two weeks in real-life conditions, which invariably included a significant amount of MNA. Our aim is to provide a framework for a novel use of a denoising autoencoder to reconstruct motion-artifact-removed PPG signals so that we can improve the AF detection performance and to increase the amount of analyzable data.We used more than 30,000 25-sec PPG segments from 129 subjects randomly selected from Pulsewatch and Stanford University’s datasets. The training and testing datasets from these two databases came from smartwatches from different vendors with varying sampling frequencies and time duration of recordings in diverse and realistic settings. In this study, the highly corrupted PPG data were automatically detected and discarded, but those segments contaminated with low-to-moderate motion and noise artifacts (MNA) were subjected to a convolutional denoising autoencoder (CDA). To reconstruct the artifact-removed PPG segments, we proposed to employ two distinct CDA models for AF and non-AF data groups initially classified as AF or non-AF. Using the proposed approach, we significantly improved the performance of detecting occult AF. We achieved classification accuracy, sensitivity, and specificity of 91.02%, 91.54%, and 90.85%, respectively, for out-of-sample test data from both databases. By sanitizing data from low-to-moderate MNA, we were able to increase the usable data coverage by 21%.

The Effects of Daubechies Wavelet Basis Function (DWBF) and Decomposition Level on the Performance of Artificial Intelligence-Based Atrial Fibrillation (AF) Detection Based on Electrocardiogram (ECG) Signals

This research studies the effects of both Daubechies wavelet basis function (DWBF) and decomposition level (DL) on the performance of detecting atrial fibrillation (AF) based on electrocardiograms (ECGs). ECG signals (consisting of 23 AF data and 18 normal data from MIT-BIH) were decomposed at various levels using several types of DWBF to obtain four wavelet coefficient features (WCFs), namely, minimum (min), maximum (max), mean, and standard deviation (stdev). These features were then classified to detect the presence of AF using a support vector machine (SVM) classifier. Distribution of training and testing data for the SVM uses the 5-fold cross-validation (CV) principle to produce optimum detection performance. In this study, AF detection performance is measured and analyzed based on accuracy, sensitivity, and specificity metrics. The results of the analysis show that accuracy tends to decrease with increases in the decomposition level. In addition, it becomes stable in various types of DWBF. For both sensitivity and specificity, the results of the analysis show that increasing the decomposition level also causes a decrease in both sensitivity and specificity. However, unlike the accuracy, changing the DWBF type causes both two metrics to fluctuate over a wider range. The statistical results also indicate that the highest AF accuracy detection (i.e., 94.17%) is obtained at the Daubechies 2 (DB2) function with a decomposition level of 4, whereas the highest sensitivity, 97.57%, occurs when the AF detection uses DB6 with a decomposition level of 2. Finally, DB2 with decomposition level 4 results in 96.750% for specificity. The finding of this study is that selecting the appropriate DL has a more significant effect than DWBF on AF detection using WCF.

Quantitative Analysis Using Consecutive Time Window for Unobtrusive Atrial Fibrillation Detection Based on Ballistocardiogram Signal.

Atrial fibrillation (AF) is the most common clinically significant arrhythmia; therefore, AF detection is crucial. Here, we propose a novel feature extraction method to improve AF detection performance using a ballistocardiogram (BCG), which is a weak vibration signal on the body surface transmitted by the cardiogenic force. In this paper, continuous time windows (CTWs) are added to each BCG segment and recurrence quantification analysis (RQA) features are extracted from each time window. Then, the number of CTWs is discussed and the combined features from multiple time windows are ranked, which finally constitute the CTW–RQA features. As validation, the CTW–RQA features are extracted from 4000 BCG segments of 59 subjects, which are compared with classical time and time-frequency features and up-to-date energy features. The accuracy of the proposed feature is superior, and three types of features are fused to obtain the highest accuracy of 95.63%. To evaluate the importance of the proposed feature, the fusion features are ranked using a chi-square test. CTW–RQA features account for 60% of the first 10 fusion features and 65% of the first 17 fusion features. It follows that the proposed CTW–RQA features effectively supplement the existing BCG features for AF detection.

An atrial fibrillation detection system based on machine learning algorithm with mix-domain features and hardware acceleration.

This paper presents a real-time electrocardiogram (ECG) analysis system that can detect atrial fibrillation (AF) using machine learning algorithms without a cloud server. The system takes advantage of the heterogeneous structure of the Zynq system-on-chip (SoC) to optimize the tasks of local implementation of AF detection. The features extraction is based on multi-domain features including entropy features and RR interval features, which is conducted using the embedded micro controller to generate significant features for AF detection. An AF classifier based on artificial neural network (ANN) algorithm is then implemented in the programmable logic of the SoC for acceleration. The validation of the proposed system is performed by using the real-world ECG data from MIT-BIH database and CPSC 2018 database. The experimental results show an accuracy 93.60% and 97.78% when tested on these two databases respectively. The AF detection performance of the embedded algorithm is majorly identical to that of the PC-based algorithm, indicating a robust performance of hardware implementation of the AF detection.

AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is characterized by the heart's beating in an uncoordinated manner. In clinical studies, patients often do not have visible symptoms during AF, and hence it is harder to detect this cardiac ailment. Therefore, automated detection of AF using the electrocardiogram (ECG) signals can reduce the risk of stroke, coronary artery disease, and other cardiovascular complications. In this paper, a novel time-frequency domain deep learning-based approach is proposed to detect AF and classify terminating and non-terminating AF episodes using ECG signals. This approach involves evaluating the time-frequency representation (TFR) of ECG signals using the chirplet transform. The two-dimensional (2D) deep convolutional bidirectional long short-term memory (BLSTM) neural network model is used to detect and classify AF episodes using the time-frequency images of ECG signals. The proposed TFR based 2D deep learning approach is evaluated using the ECG signals from three public databases. Our developed approach has obtained an accuracy, sensitivity, and specificity of 99.18% (Confidence interval (CI) as [98.86, 99.49]), 99.17% (CI as [98.85 99.49]), and 99.18% (CI as [98.86 99.49]), respectively, with 10-fold cross-validation (CV) technique to detect AF automatically. The proposed approach also classified terminating and non-terminating AF episodes with an average accuracy of 75.86%. The average accuracy value obtained using the proposed approach is higher than the short-time Fourier transform (STFT), discrete-time continuous wavelet transform (DT-CWT), and Stockwell transform (ST) based time-frequency analysis methods with deep convolutional BLSTM models to detect AF. The proposed approach has better AF detection performance than the existing deep learning-based techniques using ECG signals from the MIT-BIH database.

Signal Quality Assessment of a Novel ECG Electrode for Motion Artifact Reduction.

Background: The presence of noise is problematic in the analysis and interpretation of the ECG, especially in ambulatory monitoring. Restricting the analysis to high-quality signal segments only comes with the risk of excluding significant arrhythmia episodes. Therefore, the development of novel electrode technology, robust to noise, continues to be warranted. Methods: The signal quality of a novel wet ECG electrode (Piotrode) is assessed and compared to a commercially available, commonly used electrode (Ambu). The assessment involves indices of QRS detection and atrial fibrillation detection performance, as well as signal quality indices (ensemble standard deviation and time–frequency repeatability), computed from ECGs recorded simultaneously from 20 healthy subjects performing everyday activities. Results: The QRS detection performance using the Piotrode was considerably better than when using the Ambu, especially for running but also for lighter activities. The two signal quality indices demonstrated similar trends: the gap in quality became increasingly larger as the subjects became increasingly more active. Conclusions: The novel wet ECG electrode produces signals with less motion artifacts, thereby offering the potential to reduce the review burden, and accordingly the cost, associated with ambulatory monitoring.

Detection of Atrial Fibrillation in Short-Lead Electrocardiogram Recordings Obtained using a Smart Scale

Atrial fibrillation (AF) is the type of arrhythmia that raises possibility of severe health problems such as heart failure and stroke and it is known that a major risk factor of AF includes overweight and obesity. Based on this association between such health-related indicators, we propose a smart scale that is capable of measuring weight and electrocardiography (ECG) simultaneously. The scale was developed using Arduino Uno, a Wheatstone bridge load cell, and ECG sensors. The ECG signals were processed to compute heart rate (in other words, RR interval). The smart scale was evaluated with four healthy volunteers in terms of reliability showing high agreement with a commercial device for ECG monitoring. In addition, it implements Atrial Fibrillation (AF) detection using machine-learning classifiers including a k-Nearest Neighbor (kNN) method, a Decision Tree (DT), and a Neural Network (NN) on relatively short recordings of ECG obtained while using the scale. The root mean square of the successive differences between heart beats (RMSSD) and the Shannon entropy of the RR interval (ECG features) were extracted from ECG signals for AF detection. Performance of AF detection was tested with patients who were treated at a Cardiology Center after balancing data by applying over- and under-sampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and the Tomek Link (T-Link) algorithm. After addressing the data imbalance, the AF detection performance of each classifier (kNN, DT, and NNs) was 98.9%, 97.8%, and 98.9% respectively. This work has successfully demonstrated weight and cardio activity monitoring features while using a scale that may help keep the records of sensitive health related indexes on a daily basis.

Performance of an implantable loop recorder in the detection of heart rhythm disturbances in patients with syncope and embolic strokes

Abstract Aims The primary goal of this study was the evaluation of the arrhythmia detection performance of an implantable loop recorder (ILR) in patients with syncope of unknown etiology and patients with embolic stroke of undetermined source (ESUS). Secondary goals were the evaluation of diagnostic yield, time to diagnosis and established therapeutical consequences. Methods The Reveal LINQ ILR was implanted in n=143 patients (n=84 after ESUS, n=59 after syncope). Automatically detected episodes (n=3213) were transmitted via internet and validated by two experienced cardiologists and either classified as correct or incorrect. Positive-predictive value (ppv = true-positive episodes/true-positive episodes + false-positive episodes) was calculated for each available arrhythmia type. Incorrect episodes were classified as undersensing, oversensing, artifacts or supraventricular/ventricular ectopic beats. The diagnostic yield was defined by asystole ≥3 seconds or the detection of atrial fibrillation. Time to diagnosis was calculated as the time from implantation to detection of an asystole or atrial fibrillation. Results Every second asystole episode in syncope-patients was false-positive (ppv 52%), mostly due to undersensing. The atrial fibrillation detection performance in ESUS-patients was 70% (ppv-average). The majority of false-positive episodes was due to premature atrial and ventricular complexes (80%), followed by artifacts (14%) and oversensing (4%). Undersensing accounted for 2%. 36% of syncope-patients were diagnosed with an asystole after a mean time to event of 114 days. In 47% of syncope-patients diagnosed with asystole, a pacemaker was implanted. In 31% of ESUS-patients a diagnosis of atrial fibrillation was established, after a mean time to event of 92 days. 85% of ESUS-patients diagnosed with atrial fibrillation were ultimately treated with an oral anticoagulant. Conclusion and discussion The Reveal Linq ILR - advertised as an automatic diagnostic tool for syncope- and ESUS-patients - has its caveats. A high number of false-positive detections due to undersensing limited the asystole detection performance. The atrial fibrillation detection performance presented more reliable, despite a substantial number of false-positive detections. A time-consuming manual episode review still represents an indispensable step in the diagnostic process, and enough human resources have to be factored before starting an ILR program. Funding Acknowledgement Type of funding source: None

Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix

In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did not fully benefit from the existing prior information, resulting in unsatisfactory classification performance, especially in some applications that require high compression ratio (CR). In this paper, we propose a deep learning method to detect atrial fibrillation directly from compressed ECG without reconstruction. Specifically, we design a deep network model for one-dimensional ECG signals, and the measurement matrix is used to initialize the first layer of the model so that the proposed model can obtain more prior information which benefits improving the classification performance of atrial fibrillation detection from compressed ECG. The experimental results on the MIT-BIH Atrial Fibrillation Database show that when the CR is 10%, the accuracy and F1 score of the proposed method reach 97.52% and 98.02%, respectively. Compared with the atrial fibrillation detection from original ECG, the corresponding accuracy and F1 score are only reduced by 0.88% and 0.69%. Even at a high CR of 90%, the accuracy and F1 score are still only reduced by 6.77% and 5.31%, respectively. All of the experimental results demonstrate that the proposed method is superior to other existing methods for atrial fibrillation detection from compressed ECG. Therefore, the proposed method is promising for atrial fibrillation detection in wearable health monitoring based on compressed sensing.

Multi-task deep learning for cardiac rhythm detection in wearable devices

Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements such as step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist, where noise remains an unsolved problem. Here we develop DeepBeat, a multitask deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. The model is trained on approximately one million simulated unlabeled physiological signals and fine-tuned on a curated dataset of over 500 K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that, in comparison with a single-task model, our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of atrial fibrillation detection from a F1 score of 0.54 to 0.96. We also include in our evaluation a prospectively derived replication cohort of ambulatory participants where the algorithm performed with high sensitivity (0.98), specificity (0.99), and F1 score (0.93). We show that two-stage training can help address the unbalanced data problem common to biomedical applications, where large-scale well-annotated datasets are hard to generate due to the expense of manual annotation, data acquisition, and participant privacy.

Optimal length of R\u2013R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning

BackgroundHeartbeat interval Lorenz plot (LP) imaging is a promising method for detecting atrial fibrillation (AF) in long-term monitoring, but the optimal segment window length for the LP images is unknown. We examined the performance of AF detection by LP images with different segment window lengths by machine learning with convolutional neural network (CNN). LP images with a 32 × 32-pixel resolution of non-overlapping segments with lengths between 10 and 500 beats were created from R–R intervals of 24-h ECG in 52 patients with chronic AF and 58 non-AF controls as training data and in 53 patients with paroxysmal AF and 52 non-AF controls as test data. For each segment window length, discriminant models were made by fivefold cross-validation subsets of the training data and its classification performance was examined with the test data.ResultsIn machine learning with the training data, the averages of cross-validation scores were 0.995 and 0.999 for 10 and 20-beat LP images, respectively, and > 0.999 for 50 to 500-beat images. The classification of test data showed good performance for all segment window lengths with an accuracy from 0.970 to 0.988. Positive likelihood ratio for detecting AF segments, however, showed a convex parabolic curve linear relationship to log segment window length and peaked at 85 beats, while negative likelihood ratio showed monotonous increase with increasing segment window length.ConclusionsThis study suggests that the optimal segment window length that maximizes the positive likelihood ratio for detecting paroxysmal AF with 32 × 32-pixel LP image is 85 beats.

Convolutional Neural Network Based Detection of Atrial Fibrillation Combing R-R intervals and F-wave Frequency Spectrum.

Atrial Fibrillation (AF) is one of the arrhythmias that is common and serious in clinic. In this study, a novel method of AF classification with a convolutional neural network (CNN) was proposed, and particularly two cardiac rhythm features of R-R intervals and F-wave frequency spectrum were combined into the CNN for a good applicability of mobile application. Over 23 patients' ten-hours of Electrocardiogram (ECG) records were collected from the MIT-BIH database, and each of which was segmented into 10s-data fragments to train the designed CNN and evaluate the performance of the proposed method. Specifically, a total of 83,461 fragments were collected, 49,952 fragments of which are the normal fragments (type-N) and the others are the AF fragments. As results, the obtained average accuracy of the proposed method combining the two proposed features is 97.3%, which is shown a relative higher accuracy comparing with either that of the detection with the feature of R-R intervals (95.7%) or that with the feature of F-wave frequency spectrum (93.9%). Additionally, the sensitivity and the specificity of the present method are both of a high level of 97.4% and 97.2%, respectively. In conclusion, the CNN based approach by combining the R-R interval series and the F-wave frequency spectrum would be effectively to improve the performance of AF detection. Moreover, the proposed classification of AF with 10s-data fragments also could be potentially useful for a wearable real-time monitoring application for a pre-hospital screening of AF.

Atrial Fibrillation Detection in ICU Patients: A Pilot Study on MIMIC III Data.

Atrial fibrillation (AF) is the most prevalent arrhythmia, resulting in varying and irregular heartbeats. AF increases risk for numerous cardiovascular diseases including stroke, heart failure and as a result, computer aided efficient monitoring of AF is crucial, especially for intensive care unit (ICU) patients. In this paper, we present an automated and robust algorithm to detect AF from ICU patients using electrocardiogram (ECG) signals. Several statistical parameters including root mean square of successive differences, Shannon entropy, Sample entropy and turning point ratio are calculated from the heart rate. A subset of the Medical Information Mart for Intensive Care (MIMIC) III database containing 36 subjects is used in this study. We compare the AF detection performance of several classifiers for both the training and blinded test data. Using the support vector machine classifier with radial basis kernel, the proposed method achieves 99.95% cross-validation accuracy on the training data and 99.88% sensitivity, 99.65% specificity and 99.75% accuracy on the blinded test data.

ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention.

The complexity of the patterns associated with atrial fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the application of current signal processing and shallow machine learning approaches to accurately detect this condition. Deep neural networks have shown to be very powerful to learn the non-linear patterns in various problems such as computer vision tasks. While deep learning approaches have been utilized to learn complex patterns related to the presence of AF in electrocardiogram (ECG) signals, they can considerably benefit from knowing which parts of the signal is more important to focus on during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect the presence of AF in the ECG signals. The first channel takes in an ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the same ECG signal to consider all features of the entire signal. Besides improving detection accuracy, this model can guide the physicians via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. The experimental results confirm that the proposed model significantly improves the performance of AF detection on well-known MIT-BIH AF database with 5-s ECG segments (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40%).

A real life study, analyzing clinical and economic performance of prolonged Holter Monitoring after a cryptogenic stroke

A total of 25% to 40% of ischemic strokes are considered as cryptogenic stroke (CS) because etiology remain unclear, potentially because of misdiagnosis of Atrial Fibrillation (AF). Optimal monitoring duration to detect occult AF is debated but prospective data have demonstrated the benefits of long-term Holter Monitoring (HM). The aim of our study was to compare, in a real life study, 7 days Holter Monitoring (7DH) to 3 days Holter Monitoring (3DH) after CS, firstly in term of AF detection performance but also in term efficiency. We performed a retrospective pilot study including all consecutives patients who had 7DH, performed in our university centre, after CS. The primary end point was the presence of AF episode lasting at least 30 seconds. When AF was detected, we newly interpreted the 7DH to consider if this AF episode happened during the first 3 days of monitoring, or later. Thus each patient was compared to himself and the conditions of the exam were similar between 7DH and 3DH. From the 01/08/2015 to 01/03/2018, 217 patients with no history of AF had a 7DH for CS. Mean population age was 67 ± 10 years old, with a median CHADSVASc score of 5 (4,6) and 22% of patients had a dilated left atrium. AF was detected in 13 patients (5.9%), among them 11 were detected in the first 72 hours (5%) ( P = 0.157), which corresponded to one extra AF diagnosis every 108 HM. In term of efficiency, according to the availability, mean 7DH delay was 182 ± 380 days, which would be shorter using 3DH without additional recorders. HM reimbursement price by the Public French medical insurance is fixed at 77 euros unrelated to the monitoring duration. Finally, time consuming for the practitioner were lower according to the 3DH. This pilot study showed a very low incremental benefit of AF detection performance with a worse efficiency, considering the 7DH compare to a 3DH. However further investigations are necessary to achieve definitive conclusions.

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.

Model-Based Assessment of f-Wave Signal Quality in Patients With Atrial Fibrillation.

The detection and analysis of atrial fibrillation (AF) in the ECG is greatly influenced by signal quality. The present study proposes and evaluates a model-based f-wave signal quality index (SQI), denoted, for use in the QRST-cancelled ECG signal. is computed using a harmonic f-wave model, allowing for variation in frequency and amplitude. The properties of are evaluated on both f-waves and P-waves using 378 12-lead ECGs, 1875single-lead ECGs, and simulated signals. decreases monotonically when noise is added to f-wave signals, even for noise which overlaps spectrally with f-waves. Moreover, is shown to be closely associated with the accuracy of AF frequency estimation, where implies accurate estimation. When is used as a measure of f-wave presence, AF detection performance improves: the sensitivity increases from 97.0% to98.1% and the specificity increases from 97.4% to97.8% when compared to the reference detector. The proposed SQI represents a novel approach to assessing f-wave signal quality, as well as to determining whether f-waves are present. The use of improves the detection of AF and benefits the analysis of noisy ECGs.

RR-Interval variance of electrocardiogram for atrial fibrillation detection

Atrial fibrillation is a serious heart problem originated from the upper chamber of the heart. The common indication of atrial fibrillation is irregularity of R peak-to-R-peak time interval, which is shortly called RR interval. The irregularity could be represented using variance or spread of RR interval. This article presents a system to detect atrial fibrillation using variances. Using clinical data of patients with atrial fibrillation attack, it is shown that the variance of electrocardiographic RR interval are higher during atrial fibrillation, compared to the normal one. Utilizing a simple detection technique and variances of RR intervals, we find a good performance of atrial fibrillation detection.

Performance of Atrial Fibrillation Detection in a New Single-Chamber ICD.

Patients with implantable cardioverter defibrillators (ICDs) often have a history of atrial fibrillation (AF) or will develop AF after device implant. Optimal management of ICD patients includes early diagnosis of AF and monitoring of AF burden. We evaluated the performance of an algorithm for monitoring AF in single-chamber ICDs. The RR interval variability-based detection algorithm determines RR variability by creating a Lorenz plot of the change in RR intervals for the most recent interval pair versus the previous interval pair. A new plot is created every 2 minutes and the AF evidence score of the plot is computed. Patient RR interval data from several Holter databases were pooled to test the performance of the AF detection algorithm. In total, 187 recordings from 186 patients were used to assess the performance of the AF detection algorithm integrated into a single-chamber ICD by comparing the ICD detection results to the Holter annotated truth. Thirty-five of 186 patients had a total of 94 AF episodes in their Holter recordings lasting a total of 250.5 hours (mean episode duration 7.2 hours). The generalized estimating equations-adjusted estimate of episode sensitivity was 94.8% with 95% lower confidence limit of 87.2%. Gross duration sensitivity was 95.0% for AF episodes of at least 6 minutes duration with gross duration specificity of 99.6%. This RR interval-based AF detection algorithm performs well with high sensitivity and specificity. Integration of this algorithm into single-chamber ICDs will help monitor and detect AF, thus facilitating optimal therapy to prevent AF-related sequelae.

Rhythm-based heartbeat duration normalization for atrial fibrillation detection

BackgroundScreening of atrial fibrillation (AF) for high-risk patients including all patients aged 65 years and older is important for prevention of risk of stroke. Different technologies such as modified blood pressure monitor, single lead ECG-based finger-probe, and smart phone using plethysmogram signal have been emerging for this purpose. All these technologies use irregularity of heartbeat duration as a feature for AF detection. We have investigated a normalization method of heartbeat duration for improved AF detection. MethodAF is an arrhythmia in which heartbeat duration generally becomes irregularly irregular. From a window of heartbeat duration, we estimate the possible rhythm of the majority of heartbeats and normalize duration of all heartbeats in the window based on the rhythm so that we can measure the irregularity of heartbeats for both AF and non-AF rhythms in the same scale. Irregularity is measured by the entropy of distribution of the normalized duration. Then we classify a window of heartbeats as AF or non-AF by thresholding the measured irregularity. The effect of this normalization is evaluated by comparing AF detection performances using duration with the normalization, without normalization, and with other existing normalizations. ResultsSensitivity and specificity of AF detection using normalized heartbeat duration were tested on two landmark databases available online and compared with results of other methods (with/without normalization) by receiver operating characteristic (ROC) curves. ROC analysis showed that the normalization was able to improve the performance of AF detection and it was consistent for a wide range of sensitivity and specificity for use of different thresholds. Detection accuracy was also computed for equal rates of sensitivity and specificity for different methods. Using normalized heartbeat duration, we obtained 96.38% accuracy which is more than 4% improvement compared to AF detection without normalization. ConclusionsThe proposed normalization method was found useful for improving performance and robustness of AF detection. Incorporation of this method in a screening device could be crucial to reduce the risk of AF-related stroke. In general, the incorporation of the rhythm-based normalization in an AF detection method seems important for developing a robust AF screening device.