An Automated Algorithm for Obstructive Sleep Apnea Detection Using a Wireless Abdomen-Worn Sensor

Obstructive sleep apnea (OSA) is common among older populations and individuals with cardiovascular diseases. OSA diagnosis is primarily conducted using polysomnography or recommended home sleep apnea test (HSAT) devices. Wireless wearable devices have emerged as promising tools for OSA screening an...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 25; H. 8; S. 2412
Hauptverfasser: Dang, Thi Hang, Kim, Seong-mun, Choi, Min-seong, Hwan, Sung-nam, Min, Hyung-ki, Bien, Franklin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 10.04.2025
MDPI
Schlagworte:
Abdomen
abdomen-worn sensor
Adult
Aged
Algorithms
Artificial intelligence
capacitive sensor
Comparative analysis
Diagnosis
Female
home sleep apnea test
Humans
Male
Middle Aged
MLP-mixer
obstructive sleep apnea
Physiological aspects
Polysomnography - methods
Respiration
Sensors
Sleep apnea
Sleep apnea syndromes
Sleep Apnea, Obstructive - diagnosis
Sleep Apnea, Obstructive - physiopathology
Wearable Electronic Devices
Wireless Technology
South Korea
obstructive sleep apnea
MLP-mixer
capacitive sensor
abdomen-worn sensor
home sleep apnea test
ISSN:1424-8220, 1424-8220
Online-Zugang:Volltext
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Zusammenfassung:Obstructive sleep apnea (OSA) is common among older populations and individuals with cardiovascular diseases. OSA diagnosis is primarily conducted using polysomnography or recommended home sleep apnea test (HSAT) devices. Wireless wearable devices have emerged as promising tools for OSA screening and follow-up. This study introduces a novel automated algorithm for detecting OSA using abdominal movement signals and acceleration data collected by a wireless abdomen-worn sensor (Soomirang). Thirty-seven subjects underwent overnight monitoring using an HSAT device and the Soomirang system simultaneously. Normal and apnea events were classified using an MLP-Mixer deep learning model based on Soomirang data, which was also used to estimate total sleep time (ST). Pearson correlation and Bland–Altman analyses were conducted to evaluate the agreement of ST and the apnea–hypopnea index (AHI) calculated by the HSAT device and Soomirang. ST demonstrated a correlation of 0.9 with an average time difference of 7.5 min, while AHI showed a correlation of 0.95 with an average AHI difference of 3. The accuracy, sensitivity, and specificity of the Soomirang for detecting OSA were 97.14%, 100%, and 95.45% at AHI ≥ 15, respectively. The proposed algorithm, utilizing data from a wireless abdomen-worn device exhibited excellent performance in detecting moderate to severe OSA. The findings underscored the potential of a simple device as an accessible and effective tool for OSA screening and follow-up.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25082412