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Updated: Jun 19, 2026

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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Multi-Feature Automatic Extraction for Detecting Obstructive Sleep Apnea Based on Single-Lead Electrocardiography

Yu Zhou1, Kyungtae Kang2

  • 1Department of Computer Science and Engineering, Major in Bio Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
Summary
This summary is machine-generated.

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A new deep learning model effectively diagnoses obstructive sleep apnea (OSA) using electrocardiogram (ECG) data. This innovative method offers an accurate and convenient alternative to traditional diagnostic tools for sleep apnea.

Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Artificial Intelligence

Background:

  • Obstructive sleep apnea (OSA) is a common sleep disorder linked to cardiovascular diseases.
  • Nocturnal polysomnography (PSG) is the standard OSA diagnostic tool but is costly and time-consuming.
  • Electrocardiogram (ECG)-based methods offer potential but often require complex feature engineering.

Purpose of the Study:

  • To develop an innovative, automated method for OSA classification using ECG data.
  • To overcome the limitations of conventional OSA diagnostic techniques.
  • To enhance the accuracy and convenience of sleep apnea diagnosis.

Main Methods:

  • A composite deep convolutional neural network (CNN) model was developed for OSA classification.
Keywords:
Gramian angular fieldautomatic feature extractioncontinuous wavelet transformconvolutional neural networkdiagnosishybrid datasetobstructive sleep apnea

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Last Updated: Jun 19, 2026

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  • A multimodal strategy was employed for automatic feature extraction from ECG data.
  • ECG data was transformed into scalogram and Gramian angular field matrix images to enrich features.

    • Main Results:

    • The proposed model achieved high diagnostic performance for OSA.
    • Achieved accuracy of 96.37%, sensitivity of 94.67%, specificity of 97.44%, and AUC of 0.96.
    • The model demonstrated exceptional results on the PhysioNet Apnea-ECG database.

    Conclusions:

    • The developed deep learning model shows significant potential for efficient and accurate OSA diagnosis.
    • This approach offers a promising, convenient alternative to traditional PSG for sleep apnea detection.
    • The multimodal feature extraction strategy enhances diagnostic capabilities for OSA.