A Python analysis of MIMIC-IV (DREAMT) health data to uncover insights into factors affecting sleep disorders.
In this article, I will analyze participant information from the DREAMED dataset to discover relationships between sleep disorders such as sleep apnea, snoring, shortness of breath, headaches, restless legs syndrome (RLS) , snorts, and participant characteristics such as age, sex, body mass index (BMI), arousal index, mean oxygen saturation (Mean_SaO2), medical history, obstructive apnea-hypopnea index (OAHI), and apnea-hypopnea (IAH).
The participants here are those who participated in the DREAMED study.
The result will be a complete data analysis report with visualizations, insights and conclusions.
I will use a Jupyter notebook with Python libraries such as Pandas, Numpy, Matplotlib and Seaborn.
The data used for this analysis comes from DREAMT: Dataset for Real-Time Sleep Stage Estimation Using Multi-Sensor Wearable technology 1.0.1. DREAMED is part of the MIMIC-IV datasets hosted by PhysioNet.
