Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/app11156888
Keywords
Sleep Apnea Syndrome; tracheal sound recordings; voice activity detection algorithms; mean apnea duration index
Categories
Funding
- European Union
- Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation [T1EDK-03957]
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This study aims to detect the exact time location of apnea/hypopnea events using a Voice Activity Detection algorithm, showing good sensitivity in severe and moderate patients. The algorithm also provides a reasonable estimation of the Mean Apnea Duration index for patients with severe or moderate apnea.
The most common index for diagnosing Sleep Apnea Syndrome (SAS) is the Apnea-Hypopnea Index (AHI), defined as the average count of apnea/hypopnea events per sleeping hour. Despite its broad use in automated systems for SAS severity estimation, researchers now focus on individual event time detection rather than the insufficient classification of the patient in SAS severity groups. Towards this direction, in this work, we aim at the detection of the exact time location of apnea/hypopnea events. We particularly examine the hypothesis of employing a standard Voice Activity Detection (VAD) algorithm to extract breathing segments during sleep and identify the respiratory events from severely altered breathing amplitude within the event. The algorithm, which is tested only in severe and moderate patients, is applied to recordings from a tracheal and an ambient microphone. It proves good sensitivity for apneas, reaching 81% and 70.4% for the two microphones, respectively, and moderate sensitivity to hypopneas-approx. 50% were identified. The algorithm also presents an adequate estimator of the Mean Apnea Duration index-defined as the average duration of the detected events-for patients with severe or moderate apnea, with mean error 1.7 s and 3.2 s for the two microphones, respectively.
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