Predicting Apnea‐Hypopnea Index in Patients with Obstructive Sleep Apnea Using Unsegmented ECG‐Signal‐Based Algorithms

Author:

Chen Jeng‐Wen12,Wang Cheng‐Yi3,Lin Chun‐Cheng4,Hsu Mao‐Huan4,Yeh Cheng‐Yu4,Hwang Shaw‐Hwa5

Affiliation:

1. Department of Otolaryngology‐Head and Neck Surgery, Cardinal Tien Hospital and School of Medicine College of Medicine, Fu Jen Catholic University New Taipei City 23148 Taiwan

2. Department of Otolaryngology‐Head and Neck Surgery National Taiwan University Hospital Taipei 100225 Taiwan

3. Department of Internal Medicine, Cardinal Tien Hospital and School of Medicine College of Medicine, Fu Jen Catholic University New Taipei City 23148 Taiwan

4. Department of Electrical Engineering National Chin‐Yi University of Technology Taichung 411030 Taiwan

5. Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan

Abstract

Obstructive sleep apnea (OSA) is a common sleep disorder and is diagnosed by polysomnography (PSG) as the gold standard. However, PSG is a time‐consuming and costly test, and patients have to endure long waits before receiving a PSG test in a hospital. In view of this, portable and wearable screening tools for OSA prediction have been developed recently as a low‐cost and easy‐to‐use method. In this paper, an OSA detection model, based on regression approach, using unsegmented electrocardiography (ECG) signals is developed to directly estimate the apnea‐hypopnea index (AHI) value, which is the first report in the literature. In this manner, this work can provide more details of OSA assessment to users and doctors. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Publisher

Wiley

Subject

Electrical and Electronic Engineering

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