###### Abstract  
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. Sleep Apnea Syndrome; tracheal sound recordings; voice activity detection algorithms; mean apnea duration index +
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and intra-night variation of the AHI [16], sleep scientists suggest employing additional indices reflecting the severity of the syndrome such as: the Oxygen Desaturation Index (ODI) [17]--defined as the number of events of blood oxygen drop greater than 4% per hour of sleep--the Respiratory Distress Index (RDI) [14]--defined as the combined number of apnea and hypopnea events along with the respiratory event-related arousals per sleeping hour--and the Mean Apnea Duration (MAD) index [18]--defined as the average duration of all apneic/hypopnea events. It is also highly recommended to focus on each specific apnea/hypopnea episode rather than their average count [14]. The exact time localization of apnea/hypopnea events during the night offers the capacity to study their specific characteristics, e.g., their duration. Indeed, recent studies suggest a correlation link between the MAD with hypertension and mortality rate, for patients suffering from severe or moderate apnea syndrome [18; 19; 20]. The MAD index, despite being extracted as the average over the entire set of apnea/hypopnea events, requires a precise detection of the onset and termination of each event. The gold standard for apnea diagnosis--polysomnography (PSG)--is inconvenient for the patient while the cost for in-hospital examination of the syndrome renders remote diagnosis through accurate portable apnea detection systems imperative. Audio recording and particularly tracheal sound have been employed as a signal to detect SAS severity [21; 22; 23; 24; 25]. The recent advances in neural networks render them particularly attractive also in this field of application, providing promising results [26; 27]. Specifically, [PERSON] et al. developed a deep neural network capable of detecting sleep apnea events from tracheal sound with a detection resolution of 15 s by employing more than 1800 patients reporting accuracy of more than 90% in classifying the AHI in the four main severity groups [26]. Research by [PERSON] et al. based on machine learning techniques used 120 patients to classify the four severity groups with an accuracy of approx. 88%. In general, neural network approaches are highly dependent on the number of patients participating in the training and validation sets, while the reported accuracy is expected to be reduced when applied in the real-time processing of audio recordings. Additionally, the time detection of apnea/hypopnea events is rarely performed [26; 28; 29] with the developed diagnostic tools focusing mainly on the SAS severity estimation [21; 27]. Particularly, [PERSON] et al.