Purpose

Electrocardiogram (ECG) data obtained from 12 leads are the most common and informative source for analyzing the cardiovascular system’s (CVS) condition in medical practice. However, the large number of electrodes, specific placements on the body, and the need for specialized equipment make the ECG acquisition procedure complex and cumbersome. This raises the challenge of reducing the number of ECG leads by reconstructing missing leads based on available data.

Methods

Most existing methods for reconstructing missing ECG leads rely on utilizing signals simultaneously from multiple known leads. This study proposes a method for reconstructing ECG data in 12 leads using signal data from the first lead, lead I. Such an approach can significantly simplify the ECG registration procedure. The study demonstrates the effectiveness of using unique models with a developed architecture of artificial neural networks (ANNs) to generate the reconstructed ECG signals. Fragments of ECG from lead I, with a duration of 128 samples and a sampling frequency of 100 Hz, are input to the models. ECG fragments can be extracted from the original signal at arbitrary time points. Each model generates an ECG signal of the same length at its output for the corresponding lead.

Results

Despite existing limitations, the proposed method surpasses known solutions regarding ECG generation quality when using a single lead. The study shows that introducing an additional feature of the direction of the electrical axis of the heart (EAH) as input to the ANN models enhances the generation quality. The quality of ECG generation by the proposed ANN models is found to be dependent on the presence of CVS diseases.

Conclusions

The developed ECG reconstruction method holds significant potential for use in portable registration devices, screening procedures, and providing support for medical decision-making by healthcare specialists.

中文翻译：

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基于第一肢导联数据的 12 导联心电图重建

目的

从 12 条导联获得的心电图 (ECG) 数据是医疗实践中分析心血管系统 (CVS) 状况的最常见且信息丰富的来源。然而，大量的电极、身体上的特定位置以及对专用设备的需求使得心电图采集过程复杂且繁琐。这就提出了通过根据可用数据重建丢失的导联来减少心电图导联数量的挑战。

方法

大多数现有的重建丢失心电图导联的方法依赖于同时利用来自多个已知导联的信号。本研究提出了一种使用第一导联（导联I）的信号数据重建 12 导联 ECG 数据的方法。这种方法可以显着简化心电图注册程序。该研究证明了使用具有开发的人工神经网络 (ANN) 架构的独特模型来生成重建心电图信号的有效性。来自导联I的 ECG 片段（持续时间为 128 个样本，采样频率为 100 Hz）被输入到模型中。可以在任意时间点从原始信号中提取心电图片段。每个模型在其输出处为相应的导联生成相同长度的 ECG 信号。

结果

尽管存在局限性，但所提出的方法在使用单导联时超越了有关心电图生成质量的已知解决方案。研究表明，引入心脏电轴 (EAH) 方向的附加特征作为 ANN 模型的输入可以提高生成质量。研究发现，所提出的 ANN 模型生成心电图的质量取决于 CVS 疾病的存在。

结论

所开发的心电图重建方法在便携式登记设备、筛查程序以及为医疗保健专家的医疗决策提供支持方面具有巨大的潜力。