Sleep Disorder Detection from Heart Rate Signals

This project aims to develop an automated method for detecting sleep disorders from heart rate signals collected using a pulse oximeter. Sleep disorders can significantly impact an individual's health and quality of life. Early detection of these disorders is crucial for timely interventions and improved outcomes.

Problem Statement

• Sleep disorders are prevalent and can impact health and quality of life.
• Current methods for diagnosing sleep disorders are manual, time-consuming, and subjective.
• There is a need for an automated method to detect sleep disorders from heart rate signals to improve efficiency and accuracy.

Solution Approach

• Preprocess signals for denoising and feature extraction.
• Cluster heart rate signals using K-means clustering.
• Segment signals into shorter segments.
• Classify segments using a Convolutional Neural Network (CNN).

Installation

1. Clone the repository:

   git clone https://github.com/selcia25/sleep-disorder-detection.git
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the application:

   python app.py
   

Usage

• Upload heart rate signal data in CSV format.
• View preprocessed data and segmented signals.
• Receive classification results indicating the presence of sleep disorders.

Screenshots

License

This project is licensed under the MIT License - see the LICENSE.md file for details.