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.

中文翻译：

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生成不同心律失常的心电图模型的设计和优化


心电图 (ECG) 对于分析大多数心血管疾病具有重要的临床意义。心电图搏动形态、搏动持续时间和振幅因受试者和疾病的不同而不同。因此，基于心电形态学的建模具有长期的研究兴趣。这项工作旨在开发一个基于最少数量参数的简化心电图模型，该模型可以正确表示不同心律失常的心电图形态。提出了一种基于两个高斯函数之和的简单数学模型。然而，以确定性方式拟合多个高斯函数存在准确性和定位问题。为了解决这些拟合问题，开发了两种混合优化方法来选择最佳心电图模型参数。第一种方法是近似和全局搜索技术（ApproxiGlo）的组合，第二种方法是近似和多起点搜索技术（ApproxiMul）的组合。所提出的模型和优化方法已应用于不同心律失常的真实心电图，并在时间、频率和时频域上测量模型性能的有效性。该模型适合代表不同心律失常的不同类型心电图搏动，具有高相关系数（>0.98）。与非线性拟合方法相比，ApproxiGlo 和 ApproxiMul 的均方根误差 (RMSE) 分别提高了 3.32 倍和 7.88 倍。在优化方面，ApproxiMul 在许多指标上都比 ApproxiGlo 方法表现得更好。该模型的不同用途是可能的，例如已经开发并测试了使用图形用户界面的句法心电图生成器。 此外，该模型可以用作具有可变压缩率的有损压缩。 1 kHz 采样频率和每分钟 75 节拍可实现 20:1 的压缩比。这些优化方法可以用于使用高斯求和的不同工程领域。