Temporal-Noise-Aware Neural Networks for Suicidal Ideation Prediction Using Physiological Data

The robust generalization of deep learning models in the presence of inherent noise remains a significant challenge, especially when labels are ambiguous due to their subjective nature and noise is indiscernible in natural settings. In this article, we address a specific and important scenario of monitoring suicidal ideation (SI), where time-series data, such as galvanic skin response (GSR) and photoplethysmography (PPG), are 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 DBN_ConvNet, which integrates advanced encoding techniques with confidence learning training to enhance prediction performance. Another main contribution of our work is the collection of a specialized dataset of GSR and PPG signals derived from real-world environments for SI prediction. By employing this dataset, our DBN_ConvNet achieves a prediction accuracy of 76.67% and an F1 score of 0.74 in a binary classification task, outperforming state-of-the-art methods. Furthermore, comprehensive evaluations have been conducted on three other well-known public datasets with artificially introduced noise to test the DBN_ConvNet’s capabilities rigorously. These tests consistently demonstrated DBN_ConvNet’s superior performance by achieving an improvement of more than 10% in both accuracy and F1 score compared to the baseline methods.