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Abstract：
Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet\'s 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F\'s superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time-frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches.
摘要：
几千年来，中医一直依赖脉诊作为医疗保健评估的基石。尽管它历史悠久，用途广泛，中医脉诊由于其对主观解释和理论分析的依赖性，在诊断准确性和一致性方面面临挑战。这项研究介绍了一种方法，通过利用深度学习算法的力量来提高中医脉诊对糖尿病的准确性，特别是LeNet和ResNet模型，用于脉冲波形分析。LeNet和ResNet模型用于使用包括健康个体和糖尿病患者的不同数据集分析TCM脉搏波形。这些先进的算法与现代中医脉搏测量仪器的集成在减少依赖于医生的变异性和提高诊断的可靠性方面显示出巨大的希望。这项研究弥合了古代智慧与医疗保健尖端技术之间的差距。LeNet-F,结合基于TMC的脉冲的特殊特征提取，显示出改进的培训和测试准确性(73%和67%，分别,与LeNet的70%和65%相比）。此外，ResNet模型的表现始终优于LeNet，ResNet18-F在训练中达到最高准确率（82%），在测试中达到74%。先进的预处理技术和附加功能显著有助于ResNet18-F的卓越性能，指出了特征工程策略的重要性。此外，这项研究确定了未来研究的潜在途径，包括优化预处理技术以处理脉冲波形变化和噪声水平，整合额外的时频域特征，开发特定领域的特征选择算法，并将范围扩大到其他疾病。这些进步旨在完善中医脉诊，提高其准确性和可靠性，同时将其集成到现代技术中，以获得更有效的医疗保健方法。
