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NMNeha Malhan
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Deep Learning · Time Series · 2026

Temporal Characterization and Deep Learning-Based Modeling of Human Sleep

Modelled temporal sleep physiology from polysomnography signals and compared deep learning against gradient-boosted sequence models for sleep-stage prediction.

PythonBi-LSTMXGBoostTime Series AnalysisVARSleep-EDF

84%

Bi-LSTM accuracy

0.65

Bi-LSTM F1

74%

XGBoost accuracy

0.57

XGBoost F1

Model performance: Bi-LSTM vs. XGBoost

Problem / Research Question

Sleep staging from raw physiological signals is a sequence-classification problem where cross-signal timing matters as much as signal amplitude. The project asked whether a sequence-aware deep learning model could out-perform a strong gradient-boosted baseline on this task.

Dataset

Multivariate EEG, EOG, and EMG channels from polysomnography recordings in the Sleep-EDF dataset.

Methodology

  • 01Analyzed temporal sleep physiology using multivariate EEG, EOG, and EMG signals.
  • 02Modelled cross-signal interactions and temporal dependencies using Vector Autoregression (VAR).
  • 03Built and compared a Bi-LSTM sequence model against an XGBoost baseline for sleep-stage classification.

Models

Bi-LSTMXGBoost

Key Findings

  • The sequence-aware Bi-LSTM outperformed the XGBoost baseline on both accuracy and F1, consistent with sleep-stage transitions carrying temporal structure that a non-sequential model can't fully capture.
  • VAR modelling of cross-signal dependencies (EEG/EOG/EMG) supported the case that sleep stages are a multivariate temporal phenomenon rather than a per-timestep classification problem.

Limitations

  • F1 scores (0.65 and 0.57) are meaningfully lower than raw accuracy, indicating class imbalance across sleep stages affects both models, so accuracy alone would overstate performance.
  • Results are reported on Sleep-EDF; generalization to other recording setups or populations wasn't evaluated in this project.