A Python analysis of a MIMIC-IV health data (DREAMT) to uncover insights into factors affecting sleep disorders.
In this article, I will be analysing participants’ information from the DREAMT dataset in order to uncover relationships between sleep disorders like sleep apnea, snoring, difficulty breathing, headaches, Restless Legs Syndrome (RLS), snorting and participant characteristics like age, gender, Body Mass Index (BMI), Arousal Index, Mean Oxygen Saturation (Mean_SaO2), medical history, Obstructive apnea-hypopnea index (OAHI) and Apnea-Hypopnea Index (AHI).
The participants here are those who took part in the DREAMT study.
The outcome will be a comprehensive data analytics report with visualizations, insights, and conclusion.
I will be employing a Jupyter notebook with Python libraries like Pandas, Numpy, Matplotlib and Seaborn.
The data being used for this analysis comes from DREAMT: Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology 1.0.1. DREAMT is part of the MIMIC-IV datasets hosted by PhysioNet.