## 4 Discussion
Further analysis of the properties of the detected episodes may result in effective discrimination between true and false positive episodes, which should be among future perspectives. MAD is an index attracting increasing interest by the scientific community as it can be indicative of the severity of the syndrome and the general health status of the patients with severe or moderate SAS. Particularly, long average apnea duration is related to morning tiredness and hypertension in patients [19], as well as with significantly worse blood oxygen levels and sleep parameters such as significantly shorter sleep latency, reduced duration of NREM 3 stage, and increased arousal index [20]. The extraction of the MAD requires the accurate time localization of apnea and hypopneic events and not just an estimation of breathing sound features. In this paper, we provide an algorithmic process for an adequate estimation of the MAD of each patient. Based on tracheal sound recordings, the MAD index is underestimated for the cases of patients with a MAD greater than 20 s. This underestimation is worsened in the case of ambient microphone recordings. For only a few cases of patients with a low MAD index (10 to 20 s), there is an overestimation of the MAD index by the developed algorithm (see Supplementary Materials). For the majority of the patients (160 out of 239, 66.5%), the absolute error in the estimation of the MAD index does not exceed 5 s. This error can be partially explained by the resolution in time scoring of reference apneas in the corresponding software used by the clinical team (Sleepware G3) and the error of synchronization of the audio signals with the signals from polysomnography--estimated to be at a maximum of 2 s.  
Based on the aforementioned results, we believe that the conducted study contributes to a better understanding of the breathing sound features during an apnea/hypopnea event. The present study proves that VAD algorithms can be used with increased sensitivity in the identification of apnea/hypopnea events. The VAD algorithm employed in this study is one of the first and simplest algorithms, in terms of computational complexity, developed in the field of voice activity detection, profiting especially from the accuracy in the noise estimation. This is of crucial importance in the field of sleep breathing detection since breathing/snoring characteristics are much closer to noise than those of speech. Future steps in this field of application include testing of more sophisticated approaches, especially those depending on the latest advances in deep learning techniques [38]. Concerning the possible clinical applications, this study can prove beneficial for: (a) the inclusion of automated sound analysis as part of the laboratory PSG interpretation, indeed, for the time being, breathing audio recordings are practically excluded from the apnea events scoring process; (b) home-based systems focusing on individual events detection to screen the progress of the disease in diagnosed patients, and; (c) home-based systems aiming at identifying the syndrome's severity based on the MAD estimation. This is particularly important, taking into account that the MAD estimation is not included in home-based systems proposed in the literature, as well as the MAD is expected to be clearer when studied in the home environment than in the hospital, due to its dependence on sleep stages and sleep convenience.