This repository contains code for preprocessing Near-Infrared Spectroscopy (NIRS) and SpO2 signals, extracting features, and applying machine learning methods to detect adverse outcomes. The primary machine learning method used is XGBoost.
NIRS is a non-invasive optical technique used to monitor tissue oxygenation. This project aims to process NIRS signals to predict adverse outcomes, such as complications during medical procedures. By extracting relevant features from the signals and applying advanced machine learning methods, we aim to achieve high accuracy in outcome prediction.
- Signal_processing: Folder containing all the methods to pre-process the NIRS signals and remove the artifacts.
- Feature_extraction: All the methods to extract features from NIRS signals containing transients, signals without transients, and the transients themselves.
- Xgboost: Source code for optimizing, training, and testing XGBoost models
- utils: Utility functions
For any questions or suggestions, feel free to contact:
Minoo Ashoori - min.ashoori@gmail.com
GitHub: Minimnim