Open-source software for generating electrocardiogram signals

ECGSYN, a dynamical model that faithfully reproduces the main features of the human electrocardiogram (ECG), including heart rate variability, RR intervals and QT intervals is presented. Details of the underlying algorithm and an open-source software implementation in Matlab, C and Java are described. An example of how this model will facilitate comparisons of signal processing techniques is provided.Comment: 10 pages, 5 figure

Matlab ECG signal generator model based on frequency transformation

Предложен метод построения Matlab/Simulink модели генератора электрокардиографического сигнала на основе анализа частотного спектра и формирования соответствующих компонент, реализующих суперпозицию сигналов необходимых гармонических составляющих. Целью работы является синтез такого блока с изменяемыми параметрами, который бы мог использоваться в качестве источника сигнала при имитационном моделировании различных кардиологических систем. В ходе работы были получены решения, позволяющие генерировать кардиографический сигнал наиболее часто встречающихся патологий, моделировать вариабельность сердечного ритма и влияние наиболее распространённых помех.Electrocardiographical analysis remains an important component of the cardiovascular pathologies diagnostics. There are a number of various methods cardio-signal processing and analysis. It is beneficial to use artificial cardio-signals in addition to traditional ones. It makes it possible to set time and level parameters to simulate a broad spectrum of the normal and pathological cardiovascular conditions. This article provides the review of the existing cardio-signal simulation models. It is demonstrated that is expedient to use dynamic models that allow generation of the artificial cardio-signals with certain features to prove the effectiveness of the cardio-signal processing methods. The imitational electrocardiographical signal generator model that uses Fourier transform spectral co mponent coefficients has been suggested. The described below model has been created using Matlab/Simulink. It was determined that for the most cases it is possible to shape the imitational signal using the first fifty harmonics by utilizing a signal superposition of the mandatory harmonic components. The results of the simulation shown below proved the concept. Matlab model allows to obtain an artificial ECG signal as well as to simulate heart rate pathological conditions, heart rate variability and impact of the most common distortions. Further improvement of the imitational model is possible in the area of extending functionality as a result of changing time and level parameters of certain ECG fragments as well as the most frequently observed pathological conditions

Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks

The effectiveness of biosignal generation and data augmentation with biosignal generative models based on generative adversarial networks (GANs), which are a type of deep learning technique, was demonstrated in our previous paper. GAN-based generative models only learn the projection between a random distribution as input data and the distribution of training data.Therefore, the relationship between input and generated data is unclear, and the characteristics of the data generated from this model cannot be controlled. This study proposes a method for generating time-series data based on GANs and explores their ability to generate biosignals with certain classes and characteristics. Moreover, in the proposed method, latent variables are analyzed using canonical correlation analysis (CCA) to represent the relationship between input and generated data as canonical loadings. Using these loadings, we can control the characteristics of the data generated by the proposed method. The influence of class labels on generated data is analyzed by feeding the data interpolated between two class labels into the generator of the proposed GANs. The CCA of the latent variables is shown to be an effective method of controlling the generated data characteristics. We are able to model the distribution of the time-series data without requiring domain-dependent knowledge using the proposed method. Furthermore, it is possible to control the characteristics of these data by analyzing the model trained using the proposed method. To the best of our knowledge, this work is the first to generate biosignals using GANs while controlling the characteristics of the generated data

Data Augmentation for Generating Synthetic Electrogastrogram Time Series

Objective: To address an emerging need for large amount of diverse datasets for proper training of artificial intelligence (AI) algorithms and for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram (EGG) time series. Methods: We used EGG data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG alterations caused by the simulator sickness. Results: Proposed data augmentation method generates synthetic EGG with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in >70% cases while not accounting for individual differences. Features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. Conclusion: The code for generation of synthetic EGG time series is freely available and can be further customized to assess signal processing algorithms or to increase diversity in datasets used to train AI algorithms. The proposed approach is customized for EGG data synthesis, but can be easily utilized for other biosignals with similar nature such as electroencephalogram.Comment: three figures and two table

ECG Denoising using Angular Velocity as a State and an Observation in an Extended Kalman Filter Framework

International audienceIn this paper an efficient filtering procedure based on Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. The proposed method considers the angular velocity of ECG signal, as one of the states of an EKF. We have considered two cases for observation equations, in one case we have assumed a corresponding observation to angular velocity state and in the other case, we have not assumed any observations for it. Quantitative evaluation of the proposed algorithm on the MIT-BIH Normal Sinus Rhythm Database (NSRDB) shows that an average SNR improvement of 8 dB is achieved for an input signal of -4 dB

Deep Generative Models: The winning key for large and easily accessible ECG datasets?

Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored

Non-invasive fetal monitoring: a maternal surface ECG electrode placement-based novel approach for optimization of adaptive filter control parameters using the LMS and RLS algorithms

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size mu and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.Web of Science175art. no. 115