An Accurate and Low-power Sleep Apnea Detector in the Edge-IoT Platform

Md Abu Sayeed, Patrick Carr

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Sleep apnea (SA) is a serious sleep disorder that causes various diseases such as hypertension, type 2 diabetes, depression, and obesity. It may lead to serious health issues, including stroke if left untreated. The monitoring of SA through existing medical devices takes a lot of time and lacks the user's convenience. In this paper, an Internet of Things (IoT) enabled and automated SA detection technique has been presented that uses a deep-learning algorithm to analyze single-channel electrocardiogram (ECG) data and classify SA events from the specified epoch. SA detection is performed in two stages. In the first stage, a 1-sec epoch is extracted from the ECG signal, which is supplied for feature extraction. In the second stage, SA pattern is trained offline using a deep neural network (DNN), and real-time SA detection is performed during the testing phase. The widely available open-source dataset from PhysioNet is utilized for validation purposes. The proposed approach enhances the current state of the art and makes it a potential candidate for wearable medical devices.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023 - Proceedings
EditorsAhmed Abdelgawad, Akhtar Jamil, Alaa Ali Hameed
ISBN (Electronic)9798350322347
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023 - Mount Pleasant, United States
Duration: 16 Sep 202317 Sep 2023

Publication series

Name2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023 - Proceedings

Conference

Conference2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023
Country/TerritoryUnited States
CityMount Pleasant
Period16/09/2317/09/23

Keywords

  • Electrocardiogram (ECG)
  • Feature Reduction
  • Internet of Things (IoT)
  • Low-power Consumption
  • Sleep Apnea

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