The robust generalization of deep learning models in the presence of inherent noise remains a significant challenge, especially when labels are subjective and noise is indiscernible in natural settings. This problem is particularly pronounced in many practical applications. In this paper, we address a special and important scenario of monitoring suicidal ideation, where time-series data, such as photoplethysmography (PPG), is susceptible to such noise. Current methods predominantly focus on image and text data or address artificially introduced noise, neglecting the complexities of natural noise in time-series analysis. To tackle this, we introduce a novel neural network model tailored for analyzing noisy physiological time-series data, named TNANet, which merges advanced encoding techniques with confidence learning, enhancing prediction accuracy. Another contribution of our work is the collection of a specialized dataset of PPG signals derived from real-world environments for suicidal ideation prediction. Employing this dataset, our TNANet achieves the prediction accuracy of 63.33% in a binary classification task, outperforming state-of-the-art models. Furthermore, comprehensive evaluations were conducted on three other well-known public datasets with artificially introduced noise to rigorously test the TNANet's capabilities. These tests consistently demonstrated TNANet's superior performance by achieving an accuracy improvement of more than 10% compared to baseline methods.

翻译：深度学习模型在固有噪声条件下的鲁棒泛化能力仍是一项重大挑战，尤其是在标签具有主观性且自然环境中噪声难以辨识的场景下。该问题在诸多实际应用中尤为突出。本文聚焦监测自杀意念这一特殊且重要的应用场景，其中光电容积描记法（PPG）等时序数据极易受到此类噪声影响。现有方法主要针对图像和文本数据，或处理人为引入的噪声，忽略了时间序列分析中自然噪声的复杂性。为此，我们提出一种专用于含噪生理时序数据分析的新型神经网络模型——TNANet，该模型融合先进编码技术与置信学习，显著提升预测精度。本研究的另一贡献是采集了真实环境下用于自杀意念预测的专用PPG信号数据集。基于该数据集，我们的TNANet在二分类任务中实现了63.33%的预测准确率，超越当前最优模型。此外，我们在另外三个引入人为噪声的公开数据集上开展了全面评估，严格检验TNANet的性能。实验结果表明，TNANet相较基线方法始终展现卓越性能，准确率提升超过10%。