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Globally, it’s been approximated that over one billion individuals aged 30 and older are affected by sleep apnea, with the potential for this number to gradually decrease. This prevalence and its changing trajectory are connected to heightened detection efforts and various risk factors, including obesity, craniofacial anomalies, and upper airway irregularities. Existing research highlights certain limitations of current screening tests available in the market for identifying sleep apnea. Some tests exhibit suboptimal accuracy rates, while others might not be suitable for use in group settings. Consequently, researchers have explored the application of an AI model to enhance early diagnosis and treatment of sleep apnea. Artificial intelligence techniques like machine learning and deep learning have found application in the field of sleep disorders. This work introduces a deep neural network model for apnea detection from electrocardiogram from wearable devices. This model aims to maintain simplicity and affordability while offering improved accuracy.

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Analysis of Sleep Apnea Prediction Using 1D Convolutional Neural Network

  • 章节

  • 发表日期：

    2026年

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  • Murugan Suriya,
  • M. Menagadevi,
  • P. R. Hemalatha

摘要

Globally, it’s been approximated that over one billion individuals aged 30 and older are affected by sleep apnea, with the potential for this number to gradually decrease. This prevalence and its changing trajectory are connected to heightened detection efforts and various risk factors, including obesity, craniofacial anomalies, and upper airway irregularities. Existing research highlights certain limitations of current screening tests available in the market for identifying sleep apnea. Some tests exhibit suboptimal accuracy rates, while others might not be suitable for use in group settings. Consequently, researchers have explored the application of an AI model to enhance early diagnosis and treatment of sleep apnea. Artificial intelligence techniques like machine learning and deep learning have found application in the field of sleep disorders. This work introduces a deep neural network model for apnea detection from electrocardiogram from wearable devices. This model aims to maintain simplicity and affordability while offering improved accuracy.