Audio-Based Snoring Index in relation to AHI

• Status: 🟡 In Progress
• Duration: 21st July - ??
• Type: Data Analysis
• Repository: Repository
• Team: Fabricio

What we're exploring: The paper 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 PSG-Audio dataset.

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.

• 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).

• 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.

• The minimum interval has a strong effect.

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

• Paper Dynamics of snoring sounds and its connection with obstructive sleep apnea