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.

翻译：本研究评估了利用机器学习，特别是随机森林分类器，来区分正常与病理性吞咽声音的应用。我们采用市售可穿戴听诊器，记录了健康成年人和吞咽障碍患者的吞咽声音。分析显示，正常与病理性吞咽声在声学特征（如谱峰因子和过零率）上存在统计学显著差异，而不同流体和食物质地之间未表现出区分性差异。该系统对吞咽障碍性吞咽的敏感性（平均值±标准差：74% ± 8%）和特异性（89% ± 6%）表现尚可。模型总体准确率为83% ± 3%，F1分数为78% ± 5%。这些结果表明，机器学习可成为非侵入性吞咽障碍评估的有价值工具，但也指出了采样率限制以及区分正常与病理性声音时敏感性和特异性存在波动等挑战。本研究强调需要进一步研究以优化这些技术，促进其临床应用。