标题： 一种用于生物医学时间序列数据鲁棒深度学习分类的新数据增强策略：应用于心电图和脑电图分析 显示英文标题

标题： A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

摘要： 准确且统一分析多种生物信号（如心电图和脑电图）的需求日益增长，这对于全面的患者评估至关重要，尤其是在同步监测中。 尽管在多传感器融合方面取得了进展，但开发能够有效处理和提取基本不同生理信号特征的统一架构仍存在关键差距。 另一个挑战是许多生物医学数据集中的固有类别不平衡，这通常会导致传统方法表现偏差。 本研究通过提出一种新颖且统一的深度学习框架来解决这些问题，该框架在不同信号类型上实现了最先进的性能。 我们的方法结合了基于ResNet的卷积神经网络和注意力机制，并通过一种新的数据增强策略进行了增强：将每个信号的多个增强变体在时域上拼接以生成更丰富的表示。 与之前的工作不同，我们科学地增加了信号复杂度以实现面向未来的能力，这使得预测结果优于现有最先进方法。 预处理步骤包括小波去噪、基线去除和标准化。 通过结合这种先进的数据增强和焦点损失函数，有效地管理了类别不平衡问题。 在训练过程中应用了正则化技术以确保泛化能力。 我们严格评估了所提出的架构在三个基准数据集上的表现：UCI癫痫脑电图、MIT-BIH心律失常和PTB诊断心电图。 分别达到了99.96%、99.78%和100%的准确率，证明了在多种信号类型和临床环境中的鲁棒性。 最后，该架构需要约130 MB的内存，并且每个样本的处理时间约为10 ms，表明其适用于低端或可穿戴设备的部署。 显示英文摘要

摘要： The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring. Despite advances in multi-sensor fusion, a critical gap remains in developing unified architectures that effectively process and extract features from fundamentally different physiological signals. Another challenge is the inherent class imbalance in many biomedical datasets, often causing biased performance in traditional methods. This study addresses these issues by proposing a novel and unified deep learning framework that achieves state-of-the-art performance across different signal types. Our method integrates a ResNet-based CNN with an attention mechanism, enhanced by a novel data augmentation strategy: time-domain concatenation of multiple augmented variants of each signal to generate richer representations. Unlike prior work, we scientifically increase signal complexity to achieve future-reaching capabilities, which resulted in the best predictions compared to the state of the art. Preprocessing steps included wavelet denoising, baseline removal, and standardization. Class imbalance was effectively managed through the combined use of this advanced data augmentation and the Focal Loss function. Regularization techniques were applied during training to ensure generalization. We rigorously evaluated the proposed architecture on three benchmark datasets: UCI Seizure EEG, MIT-BIH Arrhythmia, and PTB Diagnostic ECG. It achieved accuracies of 99.96%, 99.78%, and 100%, respectively, demonstrating robustness across diverse signal types and clinical contexts. Finally, the architecture requires ~130 MB of memory and processes each sample in ~10 ms, suggesting suitability for deployment on low-end or wearable devices.