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

Modeling of the photoplethysmogram during atrial fibrillation

A phenomenological model for simulating the photoplethysmogram (PPG) during atrial fibrillation (AF) is proposed. The simulated PPG is solely based on RR interval information, and, therefore, any annotated ECG database can be used to model sinus rhythm, AF, or rhythms with premature beats. A PPG pulse is modeled by a linear combination of a log-normal and two Gaussian waveforms. The model PPG is obtained by placing individual pulses according to the RR intervals so that a connected signal is created. The model is evaluated on synchronously recorded ECG and PPG signals from the MIMIC and the University of Queensland Vital Signs Dataset databases. The results show that the model PPG signals closely resemble real signal for sinus rhythm, premature beats, as well as for AF. The model is used to study the performance of a low-complexity RR interval-based AF detector on simulated PPG signals with five different pulse types generated using the MIT–BIH AF database at signal-to-noise ratios (SNRs) from 0 to 30 dB. PPGs composed of pulses with a dicrotic notch tend to increase the rate of false alarms, especially at lower SNRs. The model is capable of generating simulated PPG signals from RR interval series with sinus rhythm, AF, and premature beats. Considering the lack of annotated, public PPG databases with arrhythmias, the simulation of realistic PPG signals based on annotated ECG signals is expected to facilitate the development and testing of PPG-specific AF detectors

Training Convolutional Neural Networks on Simulated Photoplethysmography Data : Application to Bradycardia and Tachycardia Detection

Objective: To develop a method for detection of bradycardia and ventricular tachycardia using the photoplethysmogram (PPG). Approach: The detector is based on a dual-branch convolutional neural network (CNN), whose input is the scalograms of the continuous wavelet transform computed in 5-s segments. Training and validation of the CNN is accomplished using simulated PPG signals generated from RR interval series extracted from public ECG databases. Manually annotated real PPG signals from the PhysioNet/CinC 2015 Challenge Database are used for performance evaluation. The performance is compared to that of a pulse-based reference detector. Results: The sensitivity/specificity were found to be 98.1%/97.9 and 76.6%/96.8% for the CNN-based detector, respectively, whereas the corresponding results for the pulse-based detector were 94.7%/99.8 and 67.1%/93.8%, respectively. Significance: The proposed detector may be useful for continuous, long-term monitoring of bradycardia and tachycardia using wearable devices, e.g., wrist-worn devices, especially in situations where sensitivity is favored over specificity. The study demonstrates that simulated PPG signals are suitable for training and validation of a CNN

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