通过卷积神经网络(SFD-CNN)驱动的选择性心电图特征增强瓣膜性心脏病的识别能力

Muhammad Rafli Ramadhan, Satria Mandala, Rafi Ullah, Wael M.S. Yafooz, Muhammad Qomaruddin
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引用次数: 0

摘要

瓣膜性心脏病(VHD)是导致全球死亡的一个重要原因。尽管针对这一问题开展了大量研究,但现有的瓣膜性心脏病检测结果在医学上的实际应用仍未达到最佳性能。最近针对 VHD 检测的机器学习研究取得了令人称道的准确性、灵敏度和稳健性。针对这一局限性,我们的研究提出利用卷积神经网络(SFD-CNN)驱动的选择性心电图特征来增强 VHD 检测。值得注意的是,SFD-CNN 在心音图(PCG)信号上运行,有别于基于心电图(ECG)信号的现有方法。我们提出了两个实验方案来评估 SFD-CNN 的性能:一个是默认参数条件下的方案,另一个是超参数调整后的方案。实验结果表明,SFD-CNN 超越了其他现有模型,在准确度(96.80%)、精确度(93.25%)、灵敏度(91.99%)、特异度(98.00%)和 F1 分数(92.09%)方面均有突出表现。SFD-CNN 在 VHD 检测中的出色表现表明,它在各种医疗应用中的实际应用前景广阔。它的潜力在于能够准确识别和分类 VHD,从而实现早期检测和及时干预。SFD-CNN 可以大大改善患者的预后,减轻医疗系统的负担。随着进一步的发展和完善,SFD-CNN 有可能在 VHD 检测领域掀起一场革命,成为医疗保健专业人员不可或缺的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Identification of Valvular Heart Diseases through Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN)
Valvular Heart Disease (VHD) is a significant cause of mortality worldwide. Although extensive research has been conducted to address this issue, practical implementation of existing VHD detection results in medicine still falls short of optimal performance. Recent investigations into machine learning for VHD detection have achieved commendable accuracy, sensitivity, and robustness. To address this limitation, our research proposes utilizing Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN) to enhance VHD detection. Notably, SFD-CNN operates on phonocardiogram (PCG) signals, distinguishing itself from existing methods based on electrocardiogram (ECG) signals. We present two experimental scenarios to assess the performance of SFD-CNN: one under default parameter conditions and another with hyperparameter tuning. The experimental results demonstrate that SFD-CNN surpasses other existing models, achieving outstanding accuracy (96.80%), precision (93.25%), sensitivity (91.99%), specificity (98.00%), and F1-score (92.09%). The outstanding performance of SFD-CNN in VHD detection suggests that it holds great promise for practical use in various medical applications. Its potential lies in its ability to accurately identify and classify VHD, enabling early detection and timely intervention. SFD-CNN could significantly improve patient outcomes and reduce the burden on healthcare systems. With further development and refinement, SFD-CNN has the potential to revolutionize the field of VHD detection and become an indispensable tool for healthcare professionals.
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