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首页> 美国卫生研究院文献>Journal of the Royal Society Interface >Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances
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Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

机译：根据心电图对医学进步的贡献对心电图进行分析和解释的计算技术

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Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.
机译：心电图（ECG）记录广泛用于临床筛查，可从身体表面捕获心脏的电活动。因此，心电图分析可能是帮助诊断，了解和预测导致全球30％死亡的心血管疾病的关键的第一步。计算技术，尤其是机器学习技术和计算模型，是用于分类，聚类和模拟的强大工具，并且最近已被用于解决医学数据（尤其是ECG数据）的分析。这篇综述描述了用于ECG分析的计算方法，重点是机器学习和3D计算机模拟，以及它们的准确性，临床意义以及对医学进步的贡献。第一部分着重于心跳分类以及为从常规心跳中提取异常并对其进行分类而开发的技术。第二部分着重于从整个录音中对患者的诊断，并将其应用于不同的疾病。第三部分介绍了可穿戴设备的实时诊断和应用。第四部分重点介绍了个性化ECG计算机仿真及其解释的最新领域。最后，讨论部分概述了心电图分析的挑战，并对提出的方法进行了严格的评估。这篇综述中报道的计算方法是医学发现的重要资产，并将其转化为临床领域可能会带来可喜的进展。

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