  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.
