Multimodal sleep signal tensor decomposition and hidden Markov Modeling for temazepam-induced anomalies across age groups

Background

Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss.

New method

We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18–29, 30–49, and 50–66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities.

Results

Jensen-Shannon Divergence (JSD) was employed to assess variability in hidden state transitions, with older subjects (50–66 years) under temazepam displaying heightened transition variability and network instability as indicated by a 48.57 % increase in JSD (from 0.35 to 0.52) and reductions in network density by 12.5 % (from 0.48 to 0.42) and modularity by 21.88 % (from 0.32 to 0.25). These changes reflect temazepam’s disruptive impact on sleep architecture in older adults, aligning with known age-related declines in sleep stability and pharmacological sensitivity. In contrast, younger subjects exhibited lower divergence and retained relatively stable, cyclical transition patterns. Anomaly scores further quantified deviations in state transitions, with older subjects showing increased transition uncertainty and marked deviations in REM-like to NREM state transitions.

Comparison with existing methods

This XAI-driven framework provides transparent, age-specific insights into temazepam’s impact on sleep dynamics, going beyond traditional methods by identifying subtle, pharmacologically induced changes in sleep stage transitions that would otherwise be missed.

Conclusions

DREAMS supports the development of personalized interventions based on sleep transition variability across age groups, offering a powerful tool to understand temazepam’s age-dependent effects on sleep architecture.