ECG modeling for simulation of arrhythmias in time-varying conditions

The present paper proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance

Photoplethysmogram modeling during paroxysmal atrial fibrillation : Detector evaluation

A phenomenological model for simulating photoplethys-mogram (PPG) during paroxysmal atrial fibrillation (AF) is proposed. A PPG pulse is modeled by combining a lognormal and two Gaussian waveforms. Continuous PPG signals are produced by placing and connecting individual pulses according to the RR interval pattern extracted from annotated ECG signals. This paper presents a practical application of the proposed model for studying the performance of an RR-based AF detector. Physionet databases containing AF episodes serve as a basis for modeling PPG signals. Detection performance was tested for different signal-to-noise ratios (SNRs), ranging from 0 to 30 dB. The results show that an SNR of at least 15 dB is required to ensure adequate performance. Considering the lack of annotated, public PPG databases with arrhythmias, the modeling of realistic PPGs based on annotated ECG signals should facilitate the development and testing of PPG-based detectors

Reservoir computing for extraction of low amplitude atrial activity in atrial fibrillation

A novel method for QRST cancellation during atrial fibrillation (AF) is introduced for use in recordings with two or more leads. The method is based on an echo state neural network (ESN) which estimates the time-varying, nonlinear transfer function between two leads, one lead with atrial activity and another lead without, for the purpose of canceling ventricular activity. The performance is evaluated on ECG signals, with simulated f-waves of low amplitude added, by determining the root mean square error P between the true f-wave signal and the estimated signal, as well as by evaluating the dominant AF frequency. When compared to average beat subtraction (ABS), being the most widely used method for QRST cancellation, the performance is found to be significantly better with equal to mean and standard deviation of PESN 24.8±7.3 and PABS 34.2±17.9 μV (p < 0.001). The novel method is particularly well-suited for implementation in mobile health systems where monitoring of AF during extended time periods is of interest

An Echo State Neural Network for QRST Cancellation During Atrial Fibrillation

A novel method for QRST cancellation during atrial fibrillation (AF) is introduced for use in recordings with two or more leads. The method is based on an echo state neural network which estimates the time-varying, nonlinear transfer function between two leads, one lead with atrial activity and another lead without, for the purpose of canceling ventricular activity. The network has different sets of weights that define the input, hidden, and output layers, of which only the output set is adapted for every new sample to be processed. The performance is evaluated on ECG signals, with simulated f-waves added, by determining the root mean square error between the true f-wave signal and the estimated signal, as well as by evaluating the dominant AF frequency. When compared to average beat subtraction (ABS), being the most widely used method for QRST cancellation, the performance is found to be significantly better with an error reduction factor of 0.24-0.43, depending on f-wave amplitude. The estimates of dominant AF frequency are considerably more accurate for all f-wave amplitudes than the AF estimates based on ABS. The novel method is particularly well suited for implementation in mobile health systems where monitoring of AF during extended time periods is of interest

A noise-adaptive method for detection of brief episodes of paroxysmal atrial fibrillation

The aim of this work is to develop a method for detection of brief episode paroxysmal atrial fibrillation (PAF). The proposed method utilizes four different features: RR interval irregularity, absence of P waves, presence of f-waves and noise level. The obtained features are applied to the Mamdani-type fuzzy inference method for decisionmaking. The performance was evaluated on one hundred 90 s long surrogate ECG signals with brief PAF episodes (5-30 beats). The robustness to noise in ECGs where noise level in each set is incremented in steps of 0.01 mV from 0 to 0.2 mV was examined as well. When compared to the coefficient of sample entropy, our method showed considerably better performance for low and moderate noise levels (< 0.06 mV) with an area under the receiver operating characteristic curve of 0.9 and 0.94, respectively. Similar performance is expected for higher noise levels as atrial activity is less used in the detection process. Finally, the results suggest that our method is more robust to false alarms due to ectopic beats or other irregular rhythms than the method under comparison

Modeling of the Effect of Alcohol on Episode Patterns in Atrial Fibrillation

Growing evidence shows that alcohol triggers paroxysmal atrial fibrillation (PAF) in some patients. How-ever, there is a lack of methods for assessing the causality between triggers and atrial fibrillation (AF) episodes. Accordingly, this work aims to develop an approach to episode modeling under the influence of alcohol for the purpose of evaluating causality assessment methods. The alternating, bivariate Hawkes model is used to produce episode patterns, where the conditional intensity function λ1(t) defines the transitions from sinus rhythm (SR) to AF. The effect of alcohol consumption is characterized by a body reactivity function, defined by the base intensity μ1(t), which alters λ1(t). Different AF episode patterns were modeled for alcohol units ranging from 0 to 15. The mean AF burden without alcohol was 17.2%, which doubled with 9 alcohol units; the number of AF episodes doubled from 12.9 with 8 alcohol units. The aggregation of AF episodes tended to decrease after 6 alcohol units. The proposed model of alcohol-affected PAF patterns may be useful for assessing the methods for evaluation of causality between triggers and PAF occurrence

Considerations on Performance Evaluation of Atrial Fibrillation Detectors

Objective: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. Methods: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. Results: The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. Conclusion: The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance

Electrocardiogram modeling during paroxysmal atrial fibrillation : Application to the detection of brief episodes

Objective: A model for simulating multi-lead ECG signals during paroxysmal atrial fibrillation (AF) is proposed. Significance: The model is of particular significance when evaluating detection performance in the presence of brief AF episodes, especially since annotated databases with such episodes are lacking. Approach: The proposed model accounts for important characteristics such as switching between sinus rhythm and AF, varying P-wave morphology, repetition rate of f-waves, presence of atrial premature beats, and various types of noise. Main results: Two expert cardiologists assessed the realism of simulated signals relative to real ECG signals, both in sinus rhythm and AF. The cardiologists identified the correct rhythm in all cases, and considered two-thirds of the simulated signals as realistic. The proposed model was also investigated by evaluating the performance of two AF detectors which explored either rhythm only or both rhythm and morphology. The results show that detection performance is strongly dependent on AF episode duration, and, consequently, demonstrate that the model can play a significant role in the investigation of detector properties

Model-based Assessment of f-Wave Signal Quality in Patients with Atrial Fibrillation

Objective: 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 S, for use in the QRST-cancelled ECG signal. Methods: S is computed using a harmonic f-wave model, allowing for variation in frequency and amplitude. The properties of S are evaluated on both f-waves and P-waves using 378 12-lead ECGs, 1875 single-lead ECGs, and simulated signals. Results: S decreases monotonically when noise is added to f-wave signals, even for noise which overlaps spectrally with f-waves. Moreover, S is shown to be closely associated with the accuracy of AF frequency estimation, where S&#x003E;0.3 implies accurate estimation. When S is used as a measure of f-wave presence, AF detection performance improves: the sensitivity increases from 97.0% to 98.1% and the specificity increases from 97.4% to 97.8% when compared to the reference detector. Conclusion: The proposed SQI represents a novel approach to assessing f-wave signal quality, as well as to determining whether f-waves are present. Significance: The use of S improves the detection of AF and benefits the analysis of noisy ECGs

Atrial Fibrillation Episode Patterns and Their Influence on Detection Performance

Existing studies offer little insight on how atrial fibrillation (AF) detection performance is influenced by the properties of AF episode patterns. The aim of this study is to investigate the influence of AF burden and median AF episode length on detection performance. For this purpose, three types of AF detectors, using either information on rhythm, rhythm and morphology, or ECG segments, were investigated on 1-h simulated ECGs. Comparing AF burdens of 20% and 80% for a median episode length of 167 beats, the sensitivity of the rhythm- and morphology-based detector increases only slightly whereas the specificity drops from 99.5% to 93.3%. The corresponding figures of specificity are 99.0% and 90.6% for the rhythm-based detector; 88.1% and 70.7% for the segment-based detector. The influence of AF burden on specificity becomes even more pronounced for AF patterns with brief episodes (median episode length set to 30 beats). Therefore, patterns with briefepisodes and high AF burden imply higher demands on detection performance. Future research should focus on how well episode patterns are captured