Insomnia, a prevalent sleep disorder, adversely impacts cognitive and physical health, highlighting the need for accurate and efficient diagnostic tools. This study employs multimodal biosignal analysis, integrating EEG, EMG, EOG, and ECG to classify individuals as insomniac or non-insomniac. Preprocessing techniques, such as resampling, are applied to reduce computational load, followed by feature extraction methods tailored to each signal modality. These methods capture frequency-domain, time-domain, and entropy metrics, which reveal physiological traits associated with insomnia, such as irregular brain activity, altered muscle atonia, and abnormal eye movements.
Early and late fusion techniques are employed for the classification task—early fusion achieved a better accuracy of 95%, compared to 92.275% for late fusion, with the highest overall F1 score of 95%. Explainable AI (XAI) concepts such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive Explanations) were used to determine the most influential features in classification. By leveraging multimodal data fusion, this methodology captures intricate physiological interactions to improve disease diagnosis and provides a non-invasive, interpretable detection of insomnia—paving the way for enhanced therapeutic interventions.
| File/Folder | Description |
|---|---|
BSP_1.ipynb |
Baseline models for individual signal modalities |
BSP_1_EarlyFusion.ipynb |
Implementation of Early Fusion strategy |
BSP_1_LateFusion.ipynb |
Implementation of Late Fusion strategy |
BSP-ATT.ipynb |
Attention-based fusion modeling |
Norm_BSP_1_EarlyFusion.ipynb |
Normalized Early Fusion model |
BSP_XAI.ipynb |
Explainable AI using LIME and SHAP |
combined_data.xlsx |
Dataset with extracted features |
csv_output/ |
Folder for result/output files |
README.md |
Project documentation |
The dataset used is sourced from the Sleep Disorders Research Center (SDRC). It contains recordings of 22 subjects (11 normal + 11 insomniac), sampled at 256 Hz, including:
- EEG (14 channels)
- EOG (6 channels)
- EMG (3 channels)
- ECG (1 channel)
Data was downsampled to 32 Hz and segmented into 60-second overlapping epochs for efficient processing and analysis.
- Downsampling to 32 Hz
- Epoch segmentation (60s windows with 10s overlap)
Each modality has tailored feature extraction methods:
- EEG: Wavelet Coefficients, Spectral Entropy, Sample Entropy, RMS
- ECG: HRV metrics (mean RR, SDNN, RMSSD), Entropy, RMS
- EOG: Zero-Crossing Rate, Entropy, RMS
- EMG: Hjorth Parameters, Entropy, RMS
- Early Fusion: Combines all modality features before model input
- Late Fusion: Combines predictions from modality-specific models
- Voting Classifier used for late fusion aggregation
- Models used: SVM, Random Forest, Gradient Boosting, KNN, AdaBoost, MLP, Extra Trees, SGD, Logistic Regression
To enhance interpretability:
- LIME explains individual predictions and highlights contributing features
Figure: LIME’s Prediction probabilities computed for both classes with feature values
- SHAP provides global and local interpretability of model outputs
Figure: SHAP’s Prediction probabilities computed for both clases with feature values.
- Both methods emphasize the importance of ECG and EMG features in classification
| Method | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|
| Early Fusion | 95% | 94% | 97% | 95% |
| Late Fusion | 92% | 91% | 93% | 92% |
| Best Classifiers | Random Forest, MLP, Extra Trees | All with 95% Accuracy |
Figure: Overall workflow for data preparation, feature extraction, fusion, classification, and explainability._
- Python 3.8+
numpy,pandas,matplotlib,scikit-learn,scipy- XAI:
lime,shap - Jupyter Notebook or Google Colab