QCLR: Quantum-LSTM contrastive learning framework for continuous mental health monitoring

Technologies such as Artificial Intelligence, Machine Learning, and Internet of Things has made unobtrusive mental health monitoring a reality. Since, obtaining a large-scale labelled dataset for mental health conditions is a big challenge; the self-supervised contrastive learning frameworks are more suitable for developing such systems. This paper presents a novel Quantum Long Short-Term Memory (LSTM) based Contrastive Learning framework for continuous mental health monitoring by leveraging LSTM's strengths in time series data analytics aiding it with the benefits of quantum computation, contrastive learning, and transfer learning. In the pretext task of the contrastive learning framework, a quantum guided LSTM base-encoder is developed for effective representational learning. The learnt model is then fine-tuned by training it with a small labelled dataset to further enhance its prediction capability. Experiments were carried out on seven benchmark datasets related to mental health conditions. With the enhanced representational and prediction abilities, the proposed model has shown superior performance over traditional ones. On heart rate variability dataset collected from (Schmidt et al., 2018), it achieves the greatest F1-score of 0.99. The paired t-test at 95% confidence level demonstrates that the proposed model outperforms the other related models.

Automated summary

This paper presents a Quantum LSTM-based Contrastive Learning framework for continuous mental health monitoring, which has shown superior performance in time series data analysis through experiments on seven benchmark datasets.