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The robust generalization of deep learning models in the presence of inherentnoise remains a significant challenge, especially when labels are subjectiveand noise is indiscernible in natural settings. This problem is particularlypronounced in many practical applications. In this paper, we address a specialand important scenario of monitoring suicidal ideation, where time-series data,such as photoplethysmography (PPG), is susceptible to such noise. Currentmethods predominantly focus on image and text data or address artificiallyintroduced noise, neglecting the complexities of natural noise in time-seriesanalysis. To tackle this, we introduce a novel neural network model tailoredfor analyzing noisy physiological time-series data, named TNANet, which mergesadvanced encoding techniques with confidence learning, enhancing predictionaccuracy. Another contribution of our work is the collection of a specializeddataset of PPG signals derived from real-world environments for suicidalideation prediction. Employing this dataset, our TNANet achieves the predictionaccuracy of 63.33% in a binary classification task, outperformingstate-of-the-art models. Furthermore, comprehensive evaluations were conductedon three other well-known public datasets with artificially introduced noise torigorously test the TNANet's capabilities. These tests consistentlydemonstrated TNANet's superior performance by achieving an accuracy improvementof more than 10% compared to baseline methods.

TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data