Statistical Analysis of Respiratory Data for Differentiating Patients with Wheezing and crackles.

This project analyzes a respiratory dataset to determine if there are statistically significant differences between patients with wheezing and healthy individuals. The workflow includes:

Data Preprocessing:

• Filtering: Initial filtering is performed using high-pass and low-pass filters to clean the data.

• Wavelet Decomposition: Wavelet decomposition is applied to remove cardiac rhythm artifacts from the respiratory signals.

Statistical Analysis:

• Assumptions Check: Statistical assumptions are verified to prepare for parametric testing.

• Parametric Testing: A parametric test is conducted, but the assumption of homogeneity of variances (homoscedasticity) is not met.

• Non-Parametric Testing: Given the failure to meet homoscedasticity, a non-parametric U-test is performed.

Results:

The analysis reveals significant differences between each combination of patient groups in all cases.

Key Findings:

• Significant statistical differences were observed between patients with wheezing and healthy individuals.

• Non-parametric methods provided conclusive results despite the failure of parametric assumptions.