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Sleep Apnea

Sleep Apnea

Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...

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Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea

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Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural

Huseyin Nasifoglu¹, Osman Erogul¹

¹Department of Biomedical Engineering, TOBB University of Economics and Technology, Ankara 06560, Turkey.

Physiological Measurement

|June 11, 2021

Summary

Deep learning models accurately predict obstructive sleep apnea (OSA) using electrocardiograms (ECG). CNNs automatically extract time-frequency features from ECGs, offering a reliable method for early OSA event detection.

Keywords:

convolutional neural network (CNN)electrocardiogram (ECG)obstructive sleep apnea (OSA)prediction scalogram spectrogram

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Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine

Background:

Obstructive sleep apnea (OSA) is a prevalent condition requiring effective diagnostic tools.
Traditional OSA diagnosis often involves polysomnography, which can be cumbersome.
Electrocardiograms (ECGs) offer a potential non-invasive data source for OSA prediction.

Purpose of the Study:

To compare deep convolutional neural network (CNN) models for predicting OSA using ECGs.
To investigate the efficacy of automatic time-frequency feature extraction via CNNs.
To develop and evaluate a novel CNN model for enhanced OSA prediction.

Main Methods:

ECG segments were transformed into time-frequency representations (scalograms and spectrograms) using wavelet and Fourier transforms.
AlexNet, GoogleNet, and ResNet18 models were evaluated for OSA prediction.
Transfer learning was explored, and a new, more effective CNN model was proposed.

Main Results:

The proposed CNN model achieved 82.30% accuracy, 83.22% sensitivity, and 82.27% specificity using scalograms 30 seconds before OSA onset.
Spectrogram-based prediction reached up to 80.13% accuracy and 81.99% sensitivity.
Per-recording classification demonstrated high accuracy of 91.93% for OSA events.

Conclusions:

Time-frequency features from ECG segments preceding OSA events contain valuable predictive information.
The developed CNN model serves as a robust indicator for accurate OSA prediction from ECG recordings.
Automatic feature extraction using CNNs bypasses the need for manual feature engineering in OSA detection.