## 5 Conclusions  
The algorithm used in this work is based on VAD for breathing state identification and the extraction of apnea and hypopnea events during night sleep. It proves a good sensitivity of 81% for all apnea episodes and a moderate sensitivity of 53.7% for hypopnea events when an audio breathing signal is recorded through a tracheal contact microphone. Ambient microphone recordings have also been tested and present slightly reduced sensitivity to both apneas and hypopneas. While precision is still a drawback, with false-positive detection rate being elevated, the algorithms can provide an adequate estimation of the MAD index for the patient examined. In this study, we focused on severe and moderate classes of SAS.  
Supplementary materials can be found at [[https://www.mdpi.com/article/10.3390/app11156888/s1](https://www.mdpi.com/article/10.3390/app11156888/s1)]([https://www.mdpi.com/article/10.3390/app11156888/s1](https://www.mdpi.com/article/10.3390/app11156888/s1)). Figure S1: Correlation plots of Mean Apnea Duration (MAD) index for patient with'severe' and'moderate' Sleep Apnea Syndrome (SAS), Figure S2: Performance of the provided algorithm in estimating the AHI by the use of tracheal sound recordings, Figure S3: Performance of the provided algorithm in estimating the AHI by the use of ambient microphone sound recordings, Figure S4: Box plots for the duration of all reference and detected apnea events, Figure S5: Box plot of the duration difference between the reference and the detected apnea/hypopnea events, Figure S6: Box plots for the duration difference between all reference and detected events when classified in two main categories based on their reference duration, Figure S7: Box plots for the duration difference between all reference and detected events when classified in two main categories based on the episode type, Figure S8: Box plots for the duration difference between all reference and detected events when classified in two main categories based on the body position, Figure S9: Box plots for the duration difference between all reference and detected events when classified in two main categories based on the sleep stage.  
**Author Contributions:** Conceptualization G.K., S.A.M., S.M.P.; Experiment design and setup S.A.M., N.-A.T., S.M.P.; Methodology, development and validation G.K., L.K.; Literature review G.K., L.K.; Article drafting G.K.; Critical revision of the article: S.M.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1 EDK-03957_ Automatic Pre-Hospital, In-Home, Sleep Apnea Examination).  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Local ethics committee of Sismanoglio Hospital.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The dataset \"PSG-Audio\" used in this report is publicly available at: [[http://www.dx.doi.org/10.11922/sciencedb.00345](http://www.dx.doi.org/10.11922/sciencedb.00345)]([http://www.dx.doi.org/10.11922/sciencedb.00345](http://www.dx.doi.org/10.11922/sciencedb.00345)) [32].  
**Acknowledgments:** We acknowledge the contribution of the clinical team of Sismanogleio General Hospital of Athens--[PERSON], [PERSON], [PERSON] and [PERSON]--for events annotation and characterization of all polysomnogram recordings.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.