## 3 Results  
### Demographics of the Patients Participating in Dataset  
The patients participating in this dataset were adults that followed a complete sleep study in the Sismanoglio Sleep Study Unit as a result of previous complaints of daytime fatigue and sleepiness or loud snoring. They all belonged in the categories of \"severe\" or \"moderate\" apnea patients. Table 1 presents basic anthropometric factors such as gender and the age of the patients. It is clear that the patients participating in the study covered a wide range of ages (23-88 years). The age distribution for men and women presented a similar average value and range. The values indicated in Table 1 were compared for the population of \"Severe\" and \"Moderate\" classes by the use of the Wilcoxon rank-sum test for independent samples in two populations [37], not following the normal distribution. The non-fitting with the normal distribution was verified by the use of the Kolmogorov-Smirnov test [37]. The male patients represented 74% of the total count of participating adults, which was expected due to the higher prevalence of SAS in the male population. \"Severe\" patients of SAS, based on the performed diagnosis, represented 85% of the examined population with the rest being categorized in the class of \"Moderate\". The main reason leading to this imbalance was that, in clinical practice, most of the patients seeking diagnosis who were prescribed to the PSG study, were those who present severe symptoms of apnea or have increased risk factors of suffering from the syndrome. Additionally, the presence of very few cases (less than 10) of normal participants or patients suffering from mild apnea,inevitably excluded the possibility to examine the performance of the proposed algorithm in these classes and extract safe results. Therefore, these classes were excluded, even from the demographics Table 1, while the corresponding factors and indices can be extracted by examining the complete online available dataset [32]. The Developed Apnea Detector Can Identify 73.6% and 65.3% of All Annotated Events, from Tracheal and Ambient Sound Recordings, Respectively  
The total number of annotated events in the used dataset was 57,967. When using the tracheal sound recordings, we could identify 42,671 episodes. The corresponding value of detected events for the ambient sound recordings was slightly decreased, reaching 37,969 episodes. The above sensitivity proved the dominance of the selected pattern of sound in audio recordings of apnea/hypopnea events. More precisely, this pattern was anticipated with strong frequency in all apnea events--comprising the categories of obstructive apnea, central apnea, and mixed apnea--in which the sensitivity reached 81% for the tracheal microphone and 70.4% for the ambient microphone (Table 2). The sensitivity for both microphones was much reduced in the case of hypopneas, in which the presence of loud snoring during the actual event should not only be anticipated but was also used as a characteristic for the identification of the event in manual scoring [2]. The Sensitivity Examined for Each Patient Reaches a Maximum of 99.4% of Events, however, the Mean Precision per Patient Is Kept in Moderate Level Not Exceeding 50%  
We examined the sensitivity performed by the above-described algorithmic process for each patient separately. The mean sensitivity was 71.5% for the contact microphone (tracheal recordings) and 64.1% for the ambient microphone (distant recordings). However, the maximum sensitivity per patient was 99.4% and 99.5% (Table 3) for the two microphones, respectively; we also identified patients where the sensitivity was much lower than the mean value.