Sleep Apnea Classification using Deep Learning on ECG Signals

This repository contains the implementation and results of my Master's thesis: "Sleep Apnea Classification using Deep Learning Algorithm"
submitted to the Faculty of Electrical Engineering, K. N. Toosi University of Technology, Winter 2023.

📘 Thesis Overview

Sleep apnea is a prevalent disorder with serious health implications. This work proposes a deep learning approach to classify:

• Obstructive Sleep Apnea (OSA)
• Central Sleep Apnea (CSA)
• Mixed Sleep Apnea (MSA)
• Normal Breathing

The model is trained solely on ECG signals using HRV and EDR features with an LSTM-based neural network.

📄 Full thesis available in thesis/MinaMoazami_Thesis.pdf

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🧪 Datasets

The following public datasets are used:

• MIT-BIH Polysomnographic Database
• Nationwide Children’s Hospital Sleep DataBank (NCHSDB)
• Cleveland Family Study (CFS)

Due to licensing, raw data is not included. Please refer to the links above.

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🔬 Method

This study uses publicly available polysomnography datasets to extract single-lead ECG signals. The methodology includes:

• Signal Processing: Extracting HRV (Heart Rate Variability) and EDR (ECG-Derived Respiration) features from filtered ECG signals using NeuroKit2.
• Segmentation: Dividing signals into 60-second non-overlapping epochs with corresponding apnea labels.
• Feature Engineering: Time-domain, frequency-domain, and non-linear HRV features were combined with EDR patterns.
• Modeling: A stacked LSTM (Long Short-Term Memory) model was designed to learn temporal dynamics and classify each epoch into:
  • Normal Breathing
  • Obstructive Sleep Apnea (OSA)
  • Central Sleep Apnea (CSA)
  • Mixed Sleep Apnea (MSA)

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📊 Results

The proposed model achieved high performance on multi-class classification tasks using ECG data alone:

Metric	Value
Precision	0.721
Recall	0.709
F1 Score	0.699
AUC (avg.)	0.894

Class-wise performance showed that the model performs best for distinguishing Normal and OSA events, with slightly lower precision on MSA due to class imbalance.

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📁 Repository Contents

• notebooks/: Jupyter Notebooks for data preprocessing, feature extraction, and model training
• thesis/: Final PDF of the thesis
• models/: Optionally include saved models or logs here

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📦 Requirements

Install required libraries via:

pip install -r requirements.txt