A piecewise spline approach for modeling of ECG signals

This paper presents a new spline-based modeling method of electrocardiogram (ECG) signal that can reproduce normal as well as abnormal ECG beats. Large volume ECG data is required for automatic machine learning diagnostic systems, medical education, research and testing purposes but due to privacy issues, access to this medical data is very difficult. Given this, modeling an ECG signal is a very challenging task in the field of biomedical signal processing. Spline-based modeling is the latest and one of the most efficient methods with very low computational complexity in the domain of ECG signal generation. In this paper, healthy ECG and arrhythmia conditions have been considered for the synthetic generation, (namely Atrial fibrillation and Congestive heart failure ECG beats) because these are the leading causes of death globally. To validate the performance of the presented modeling method, it is tested on 100 signals, also the percentage root mean square difference (PRD) and the root mean square error (RMSE) have been determined. These calculated values are analyzed and the results are found to be very promising and show that the presented method is one of the best methods in the field of synthetic ECG signal generation. A comparison amongst relevant existing techniques and the proposed method is also presented. The performance merit values PRD and RMSE, for the proposed method obtained are 38.99 and 0.10092, respectively, which are lower than the values obtained in other compared methods. To ensure fidelity of the proposed modeling technique with respect to IEC60601 standard, few Conformance Testing Services (CTS)database signals have also been modelled with a very close resemblance with the standard signals.

The Application of Computer Techniques to ECG Interpretation

It is over 120 years since Einthoven introduced the electrocardiogram [...]

Automated ECG Interpretation—A Brief History from High Expectations to Deepest Networks

This article traces the development of automated electrocardiography from its beginnings in Washington, DC around 1960 through to its current widespread application worldwide. Changes in the methodology of recording ECGs in analogue form using sizeable equipment through to digital recording, even in wearables, are included. Methods of analysis are considered from single lead to three leads to twelve leads. Some of the influential figures are mentioned while work undertaken locally is used to outline the progress of the technique mirrored in other centres. Applications of artificial intelligence are also considered so that the reader can find out how the field has been constantly evolving over the past 50 years.