Med Biol Eng Comput - Detection of swallows with silent aspiration using swallowing and breath sound analysis.

Tópicos

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Resumo

In this study, the feasibility of acoustical analysis for detection of swallowing silent aspiration is investigated. As a pilot study, we analyzed the breath sounds of 21 dysphagic individuals, 11 of which demonstrated aspiration during the fiberoptic endoscopic evaluation of swallowing (FEES) or videofluoroscopic swallowing study (VFSS). We found that the low frequency components of the power spectrum of the breath sounds after a swallow show higher magnitude when there is aspiration. Thus, we divided the frequency range below 300 Hz into three sub-bands and calculated the average power of the breath sound signal in each sub-band as the characteristic features for the stage 1 classification into two groups of aspirated and non-aspirated patients. Then, for the aspirated group, the unsupervised fuzzy k-means clustering algorithm was deployed to label the breath sounds immediately after a swallow as aspiration or non-aspiration. The results were compared with the FEES/VFSS assessments provided by the speech language pathologists. The results are encouraging: more than 86 % accuracy in detection of silent aspiration. While the proposed method should be verified on a larger dataset, the results are promising for the use of acoustical analysis as a clinical tool to detect silent aspiration.

Resumo Limpo

studi feasibl acoust analysi detect swallow silent aspir investig pilot studi analyz breath sound dysphag individu demonstr aspir fiberopt endoscop evalu swallow fee videofluoroscop swallow studi vfss found low frequenc compon power spectrum breath sound swallow show higher magnitud aspir thus divid frequenc rang hz three subband calcul averag power breath sound signal subband characterist featur stage classif two group aspir nonaspir patient aspir group unsupervis fuzzi kmean cluster algorithm deploy label breath sound immedi swallow aspir nonaspir result compar feesvfss assess provid speech languag pathologist result encourag accuraci detect silent aspir propos method verifi larger dataset result promis use acoust analysi clinic tool detect silent aspir

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