====== Audio-Based Snoring Index in relation to AHI ======

===== Quick Summary =====


  * **Status**: 🟡 In Progress 
  * **Duration**: 21st July - ??  
  * **Type**: Data Analysis
  * **Repository**: [[https://code.rekonas.com/fabricio/ml-exploratory-snore-intensity-AHI|Repository]]  
  * **Team**: Fabricio

**What we're exploring**:
The paper [[https://www.sciencedirect.com/science/article/pii/S0378437112008011|Dynamics of snoring sounds and its connection with obstructive sleep apnea]] showcases a significant correlation between the number of snore events per hour (computed from audio recordings) and the AHI. We try to reproduce the results on the [[https://www.nature.com/articles/s41597-021-00977-w|PSG-Audio]] dataset. 


===== Environment & Setup =====

** Data ** 
  * Data was downloaded from our minio bucket `rekonas-dataset-psg-audio`.
  * I made direct use of the extracted FLAC audio files provided at some point by Corius at `rekonas-dataset-psg-audio/cornelius.reyneke/psg_audio_V3/APNEA_FLAC`. I did not try to reproduce it, and unfortunately no scripts for the extraction of the FLACs from the EDFs is available.
  * For the processing of this data I provide a few python scripts with included dependencies (runnable via `uv run`). These a.) filter the data using a bandpass filter and b.) extract the apnea/hypopnea events and sleep stages from the XMLs (somehow called `.rml`) `rekonas-dataset-psg-audio/psg_audio_V3/APNEA_RML_clean`.

Notes on the XMLS/Anonotations:
  * The XMLs only include the onset but not the duration of sleep stages, and can thus not be parsed by our internal RKNS, a custom XML parser was required.
  * The XMLs work with namespaces, which makes parsing difficult.
  * Sleep stages are available from both experts and automatic staging for this dataset. I used the expert-based stages. 
  * For a few recordings no corresponding FLAC is available, Corius mentioned that a few failed during his extraction.


===== Key Findings =====
==== What Worked ====
  * There is a significant correlation between the extracted snore index and the AHI
  * The correlation seems strongest, when one considers only snores that are at least 20s later than the last snore (instead of the proposed 10s in the paper). 
  * The correlation seems strongest for N2, but the caveat is that there is a lack of data for other stages, as the patients slept badly (~4hrs total sleep time on average).
  
==== What Didn't Work ====
  * There's no proper explanation as to why the 20s minimum interval increases correlation.
  * The filtering described in the paper doesn't work well for the available audio data. I instead used a basic butterworth filter of a lower order (12). This could definitely be improved
  * Talking and other sounds are not really filtered out by the filtering + thresholding approach.
  * The threshold is selected absolutely arbitrarily.

==== Surprises & Insights ====
  * The minimum interval has a strong effect.

===== Recommendations =====
**Should we proceed?**: Maybe

**If yes, what needs to happen?**

  * Include knowledge about the SPO2 measurements for the selection of snores.
  * Consider using a proper snore detection method
  * Instead of simply counting snores, a smarter aggregation of the events should be considered. The sensitivity to the low interval suggests that the intervals are highly relevant. Maybe consider frequency of snore events?
  * Consider using the machine labels 
  * Look at correlation with only apneas and only hypopneas respectively.


===== External Resources =====
  * Paper [[https://www.sciencedirect.com/science/article/pii/S0378437112008011|Dynamics of snoring sounds and its connection with obstructive sleep apnea]]
  * 
===== Images =====
{{ :poc:2025:audio_snoreindex_r2_comparison_overview_combined.png?600 | }}

{{ :poc:2025:audio_snoreindex_audio_subset_snoring.png?600 | }}


{{ :poc:2025:audio_snoreindex_stii_bystages.png?1200 | }}