A Python evaluation of a MIMIC-IV health data (DREAMT) to uncover insights into aspects affecting sleep disorders.
In this text, I will probably be analysing participants’ information from the DREAMT dataset to be able to uncover relationships between sleep disorders like sleep apnea, snoring, difficulty respiration, 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 listed below are those that took part within the DREAMT study.
The end result will probably be a comprehensive data analytics report with visualizations, insights, and conclusion.
I will probably be employing a Jupyter notebook with Python libraries like Pandas, Numpy, Matplotlib and Seaborn.
The info getting used for this evaluation comes from DREAMT: Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology 1.0.1. DREAMT is an element of the MIMIC-IV datasets hosted by PhysioNet.
