A Recall-First CNN for Sleep Apnea Screening from Snoring Audio
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912620371509248 |
|---|---|
| author | Mallick, Anushka Noorain, Afiya Menon, Ashwin Solanki, Ashita Balaji, Keertan |
| author_facet | Mallick, Anushka Noorain, Afiya Menon, Ashwin Solanki, Ashita Balaji, Keertan |
| contents | Sleep apnea is a serious sleep-related breathing disorder that is common and can impact health if left untreated. Currently the traditional method for screening and diagnosis is overnight polysomnography. Polysomnography is expensive and takes a lot of time, and is not practical for screening large groups of people. In this paper, we explored a more accessible option, using respiratory audio recordings to spot signs of apnea.We utilized 18 audio files.The approach involved converting breathing sounds into spectrograms, balancing the dataset by oversampling apnea segments, and applying class weights to reduce bias toward the majority class. The model reached a recall of 90.55 for apnea detection. Intentionally, prioritizing catching apnea events over general accuracy. Despite low precision,the high recall suggests potential as a low-cost screening tool that could be used at home or in basic clinical setups, potentially helping identify at-risk individuals much earlier. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00052 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Recall-First CNN for Sleep Apnea Screening from Snoring Audio Mallick, Anushka Noorain, Afiya Menon, Ashwin Solanki, Ashita Balaji, Keertan Sound Machine Learning Audio and Speech Processing Sleep apnea is a serious sleep-related breathing disorder that is common and can impact health if left untreated. Currently the traditional method for screening and diagnosis is overnight polysomnography. Polysomnography is expensive and takes a lot of time, and is not practical for screening large groups of people. In this paper, we explored a more accessible option, using respiratory audio recordings to spot signs of apnea.We utilized 18 audio files.The approach involved converting breathing sounds into spectrograms, balancing the dataset by oversampling apnea segments, and applying class weights to reduce bias toward the majority class. The model reached a recall of 90.55 for apnea detection. Intentionally, prioritizing catching apnea events over general accuracy. Despite low precision,the high recall suggests potential as a low-cost screening tool that could be used at home or in basic clinical setups, potentially helping identify at-risk individuals much earlier. |
| title | A Recall-First CNN for Sleep Apnea Screening from Snoring Audio |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.00052 |