Department of Electrical and Electronics Engineering, Bhilai Institute of Technology Durg, Durg 491001, India.
Department of Mechanical Engineering, Bhilai Institute of Technology Durg, Durg 491001, India.
J Electrocardiol. 2024 Jul-Aug;85:19-24. doi: 10.1016/j.jelectrocard.2024.05.086. Epub 2024 May 21.
The heart's study holds paramount importance in human physiology, driving valuable research in cardiovascular health. However, assessing Electrocardiogram (ECG) analysis techniques poses challenges due to noise and artifacts in authentic recordings. The advent of machine learning systems for automated diagnosis has heightened the demand for extensive data, yet accessing medical data is hindered by privacy concerns. Consequently, generating artificial ECG signals faithful to real ones is a formidable task in biomedical signal processing. This paper introduces a method for ECG signal modeling using parametric quartic splines and generating a new dataset based on the modeled signals. Additionally, it explores ECG classification using three machine learning techniques facilitated by Orange software, addressing both normal and abnormal sinus rhythms. The classification enables early detection and prediction of heart-related ailments, facilitating timely clinical interventions and improving patient outcomes. The assessment of synthetic signal quality is conducted through power spectrum analysis and cross-correlation analysis, power spectrum analysis of both real and synthetic ECG waves provides a quantitative assessment of their frequency content, aiding in the validation and evaluation of synthetic ECG signal generation techniques. Cross-correlation analysis revealing a robust correlation coefficient of 0.974 and precise alignment with a negligible time lag of 0.000 s between the synthetic and real ECG signals. Overall, the adoption of quartic spline interpolation in ECG modeling enhances the precision, smoothness, and fidelity of signal representation, thereby improving the effectiveness of diagnostic and analytical tasks in cardiology. Three prominent machine learning algorithms, namely Decision Tree, Logistic Regression, and Gradient Boosting, effectively classify the modeled ECG signals with classification accuracies of 0.98620, 0.98965, and 0.99137, respectively. Notably, all models exhibit robust performance, characterized by high AUC values and classification accuracy. While Gradient Boosting and Logistic Regression demonstrate marginally superior performance compared to the Decision Tree model across most metrics, all models showcase commendable efficacy in ECG signal classification. The study underscores the significance of accurate ECG modeling in health sciences and biomedical technology, offering enhanced accuracy and flexibility for improved cardiovascular health understanding and diagnostic tools.
心脏研究在人类生理学中至关重要，推动了心血管健康的有价值研究。然而，由于真实记录中的噪声和伪影，评估心电图（ECG）分析技术具有挑战性。用于自动诊断的机器学习系统的出现增加了对大量数据的需求，但由于隐私问题，访问医疗数据受到阻碍。因此，生成与真实 ECG 信号忠实相符的人工 ECG 信号是生物医学信号处理中的一项艰巨任务。本文介绍了一种使用参数四次样条进行 ECG 信号建模的方法，并基于建模信号生成了一个新数据集。此外，它还探索了使用 Orange 软件支持的三种机器学习技术进行 ECG 分类，包括正常和异常窦性节律。该分类可实现对心脏相关疾病的早期检测和预测，促进及时的临床干预并改善患者预后。通过功率谱分析和互相关分析来评估合成信号的质量，对真实和合成 ECG 波的功率谱分析提供了对其频率内容的定量评估，有助于验证和评估合成 ECG 信号生成技术。互相关分析显示出稳健的相关系数为 0.974，并且在合成和真实 ECG 信号之间存在可忽略的时间滞后为 0.000 s。总体而言，在 ECG 建模中采用四次样条插值可提高信号表示的精度、平滑度和保真度，从而提高心脏病学中的诊断和分析任务的效果。三种突出的机器学习算法，即决策树、逻辑回归和梯度提升，分别有效地对建模的 ECG 信号进行分类，分类准确率分别为 0.98620、0.98965 和 0.99137。值得注意的是，所有模型都表现出稳健的性能，具有高 AUC 值和分类准确性。虽然在大多数指标上，梯度提升和逻辑回归与决策树模型相比表现出略微优越的性能，但所有模型在 ECG 信号分类方面都表现出令人赞赏的效果。该研究强调了在健康科学和生物医学技术中进行准确 ECG 建模的重要性，为改善心血管健康理解和诊断工具提供了更高的准确性和灵活性。
