I developed a novel Depth of Anesthesia (DoA) index using EEG-derived features to enhance intraoperative patient monitoring in clinical settings. The model was designed to produce BIS-like index values ranging from 0 (deep anesthesia) to 100 (awake).

The project involved rigorous feature selection and model validation using Pearson correlation, R², and Bland-Altman plots to ensure robustness on withheld test data. Supervised machine learning techniques including Linear Regression and Random Forest were employed to model the index from EEG features (x1–x7).

For a separate dataset containing 4,965 EEG segments, I implemented unsupervised learning algorithms (K-means clustering and Gaussian Mixture Models) to identify anesthesia states without direct supervision, labeling them as states A/B to infer depth.

The final solution integrated both supervised and unsupervised approaches into a hybrid ensemble model that combined linear regression and random forest using weighted averaging predictors, resulting in enhanced predictive accuracy and system resilience.