Department of Mechatronics, School of Intelligent Systems, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran.
School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, 2350, Australia.
Comput Biol Med. 2024 Nov;182:109126. doi: 10.1016/j.compbiomed.2024.109126. Epub 2024 Sep 9.
Cardiovascular diseases represent the leading global cause of death, typically diagnosed and addressed through electrocardiograms (ECG), which record the heart's electrical activity. In recent years, there has been a notable surge in ECG recordings, driven by the widespread use of wearable devices. However, the limited availability of medical experts to analyze these recordings underscores the necessity for automated ECG analysis using computer-aided methods. In this study, we introduced 3DECG-Net, a deep learning model designed to detect and classify seven distinct heart states through the analysis of data fusion from 12-lead ECG in a multi-label framework. Our model leverages a residual architecture with a multi-head attention mechanism, undergoing training within a five-fold cross-validation scheme. By transforming 12-lead ECG signals into 3D data with the help of Recurrent Plot technique, 3DECG-Net achieves a noteworthy micro F1-score of 80.3 %, surpassing the performance of other state-of-the-art deep learning models developed for this specific task. Also, we present an ECG preprocessing framework to generate compact, high-quality ECG signals for potential application in future studies within this domain. We conduct an explainable AI experiment using Local Interpretable Model-agnostic Explanations (LIME) to elucidate the significance of each lead in accurately diagnosing specific arrhythmias, ensuring the logical processing of ECG data by 3DECG-Net. The findings of this study suggest that the proposed model is trustworthy and has the potential to be used as an effective diagnostic toolset for identifying heart arrhythmias. Its effectiveness can improve the diagnostic process, facilitate early treatment, and enhance overall efficiency in medical settings.
心血管疾病是全球主要的死亡原因，通常通过心电图（ECG）进行诊断和治疗，心电图记录心脏的电活动。近年来，由于可穿戴设备的广泛使用，心电图记录数量显著增加。然而，由于医疗专家有限，无法分析这些记录，因此需要使用计算机辅助方法进行自动化心电图分析。在这项研究中，我们引入了 3DECG-Net，这是一种深度学习模型，旨在通过在多标签框架中分析来自 12 导联心电图的数据融合，检测和分类七种不同的心脏状态。我们的模型利用具有多头注意力机制的残差结构，在五重交叉验证方案中进行训练。通过使用 Recurrent Plot 技术将 12 导联心电图信号转换为 3D 数据，3DECG-Net 实现了微 F1 得分为 80.3%的显著成绩，超过了为该特定任务开发的其他最先进的深度学习模型的性能。此外，我们还提出了一种心电图预处理框架，用于生成紧凑、高质量的心电图信号，以便在该领域的未来研究中潜在应用。我们使用局部可解释模型不可知解释（LIME）进行可解释人工智能实验，以阐明每个导联在准确诊断特定心律失常中的重要性，确保 3DECG-Net 对心电图数据进行逻辑处理。这项研究的结果表明，所提出的模型是可靠的，并且有可能作为识别心脏心律失常的有效诊断工具集。它的有效性可以提高诊断过程的效率，促进早期治疗，并提高医疗环境的整体效率。
