Sleep Apnea Detection from Respiratory Sounds: Dataset Documentation

This repository contains the complete processed data suite, computational artifacts, and reproducibility materials associated with the study entitled "Sleep apnea detection from respiratory sounds using time–Frequency features and neural networks." This dataset supports the development, internal validation, and independent replication of a lightweight deep learning model for binary classification of sleep apnea events using respiratory audio-derived features.
Important Note on Raw Audio: Raw respiratory audio files (.wav) are not included in this repository. These have been previously deposited and are publicly accessible via the associated GitHub repository: https://github.com/SebasTS15/Sleep-Apnea-Detection-Model. This Mendeley Data repository focuses on processed feature matrices, model weights, training logs, and all numerical data underlying the manuscript figures to facilitate reproducibility without redistributing large audio files.