Key result
Novel Gaussian ECG modeling reduces reconstruction error ~8-fold vs. standard nonlinear fitting.
Why the study?
ECG morphology varies across subjects and diseases, and fitting deterministic multi-Gaussian models has accuracy and localization problems.
Effect estimate: 7.88 times better (ApproxiMul)
A novel ECG modeling technique using the sum of two Gaussian functions and hybrid optimization significantly improves the morphological fitting accuracy of various cardiac dysrhythmias compared to traditional non-linear methods.
May streamline ECG modeling in research; leaves open clinical validation for dysrhythmia applications.
The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.
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Awal et al. (2021) studied Cardiac dysrhythmias (ECG modeling). Sum of two Gaussians ECG model optimized via ApproxiGlo and ApproxiMul vs. Nonlinear fitting method was evaluated on Root mean square error (RMSE) (7.88 times better (ApproxiMul)). The proposed ECG model based on the sum of two Gaussian functions, optimized via ApproxiMul, fit different types of ECG beats with high correlation coefficients (>0.98) and reduced RMSE 7.88-fold compared to nonlinear fitting.
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