Cervical Auscultation Machine Learning for Dysphagia Assessment
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arXiv
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| Format: | Preprint |
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2024
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| author | Chia, An An Lum, Stacy Boo, Michelle Tan, Rex T, Balamurali B Chen, Jer-Ming |
| author_facet | Chia, An An Lum, Stacy Boo, Michelle Tan, Rex T, Balamurali B Chen, Jer-Ming |
| contents | This study evaluates the use of machine learning, specifically the Random Forest Classifier, to differentiate normal and pathological swallowing sounds. Employing a commercially available wearable stethoscope, we recorded swallows from both healthy adults and patients with dysphagia. The analysis revealed statistically significant differences in acoustic features, such as spectral crest, and zero-crossing rate between normal and pathological swallows, while no discriminating differences were demonstrated between different fluidand diet consistencies. The system demonstrated fair sensitivity (mean plus or minus SD: 74% plus or minus 8%) and specificity (89% plus or minus 6%) for dysphagic swallows. The model attained an overall accuracy of 83% plus or minus 3%, and F1 score of 78% plus or minus 5%. These results demonstrate that machine learning can be a valuable tool in non-invasive dysphagia assessment, although challenges such as sampling rate limitations and variability in sensitivity and specificity in discriminating between normal and pathological sounds are noted. The study underscores the need for further research to optimize these techniques for clinical use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05870 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cervical Auscultation Machine Learning for Dysphagia Assessment Chia, An An Lum, Stacy Boo, Michelle Tan, Rex T, Balamurali B Chen, Jer-Ming Sound Human-Computer Interaction Audio and Speech Processing This study evaluates the use of machine learning, specifically the Random Forest Classifier, to differentiate normal and pathological swallowing sounds. Employing a commercially available wearable stethoscope, we recorded swallows from both healthy adults and patients with dysphagia. The analysis revealed statistically significant differences in acoustic features, such as spectral crest, and zero-crossing rate between normal and pathological swallows, while no discriminating differences were demonstrated between different fluidand diet consistencies. The system demonstrated fair sensitivity (mean plus or minus SD: 74% plus or minus 8%) and specificity (89% plus or minus 6%) for dysphagic swallows. The model attained an overall accuracy of 83% plus or minus 3%, and F1 score of 78% plus or minus 5%. These results demonstrate that machine learning can be a valuable tool in non-invasive dysphagia assessment, although challenges such as sampling rate limitations and variability in sensitivity and specificity in discriminating between normal and pathological sounds are noted. The study underscores the need for further research to optimize these techniques for clinical use. |
| title | Cervical Auscultation Machine Learning for Dysphagia Assessment |
| topic | Sound Human-Computer Interaction Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.05870 |