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Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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AI-Enabled Algorithm for Automatic Classification of Sleep Disorders Based on Single-Lead Electrocardiogram.

Erdenebayar Urtnasan1, Eun Yeon Joo2, Kyu Hee Lee1

  • 1Artificial Intelligence Bigdata Medical Center, Wonju College of Medicine, Yonsei University, Wonju 26417, Korea.

Diagnostics (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

An AI algorithm called the sleep disorder network (SDN) accurately classifies four major sleep disorders using single-lead electrocardiogram (ECG) signals. This AI-enabled method offers a promising tool for sleep disorder screening and monitoring.

Keywords:
automatic classificationconvolutional neural networkdeep learningelectrocardiogramsleep disorders

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Sleep Medicine

Background:

  • Healthy sleep is crucial for overall well-being, yet sleep disorders significantly impair sleep quality and duration.
  • Accurate and convenient methods for detecting and classifying sleep disorders are essential for timely intervention and management.

Purpose of the Study:

  • To develop and validate an AI-enabled algorithm for the automatic classification of four major sleep disorders using single-lead ECG signals.
  • To assess the feasibility of using ECG data alone for sleep disorder identification.

Main Methods:

  • An AI algorithm, the sleep disorder network (SDN), was designed using deep convolutional neural networks.
  • The SDN was trained and evaluated on single-lead ECG signals from 35 subjects (control and four sleep disorder groups) in the CAP Sleep Database.
  • The algorithm was optimized using dropout and batch normalization, with ECG data pre-processed and segmented into 30-second intervals.

Main Results:

  • The SDN achieved high classification performance across all groups.
  • Specific F1 scores included 99.0% for control (CNT), 97.0% for insomnia (INS), 97.0% for periodic leg movement (PLM), 95.0% for REM sleep behavior disorder (RBD), and 98.0% for nocturnal frontal-lobe epilepsy (NFE).

Conclusions:

  • The proposed AI-enabled SDN algorithm demonstrates high accuracy in classifying sleep disorders from single-lead ECG signals.
  • This method presents a potential non-invasive screening tool for sleep disorders, utilizing only ECG data for monitoring.