Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.

Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical-grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami-structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high-quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post-stroke patients captures the system's significance in measuring multiple physiological signals in real-time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non-invasive alternative for monitoring swallowing and aspiration events.

Effect of Behavioral Cough Suppression Therapy Delivered via Telehealth.

OBJECTIVES/HYPOTHESIS: Behavioral cough suppression therapy (BCST) has demonstrated up to 88% effectiveness at treating refractory chronic cough (RCC). With onset of the COVID-19 pandemic, along with many other medical services, BCST shifted to telehealth delivery. Our group hypothesized that BCST delivered via telemedicine by a specialized Speech-Language Pathologist would be comparable to previously reported response to treatment for in-person settings. STUDY DESIGN: Retrospective review. METHODS: An Emory IRB approved, retrospective review of electronic medical records was completed for RCC patients who received BCST via telehealth from March 2020 through January 2022 at Emory Voice Center. Patients were included in the study if they had a diagnosis of RCC, were referred for BCST, were seen for at least one therapy session in the telehealth setting, and provided Cough Severity Index (CSI) data pre and post-treatment. Patients were excluded if they had incomplete datasets, a known pulmonary condition, structural laryngeal disorders, smoking history, dysphagia, and ACE-inhibitor use. Change in CSI score pre- and post-treatment was calculated to determine treatment effect. Paired-samples t-tests were conducted to compare pre-and post-treatment CSI score change. RESULTS: Fifty-one RCC patients were included in this study; 88% were female with an average age of 60 years (SD = 12.68). Post-treatment CSI scores were significantly lower than pretreatment CSI scores (P < 0.0001). These findings are comparable to historical documented CSI change achieved with in-person BCST. CONCLUSIONS: This study provides preliminary evidence of the efficacy of BCST via telehealth for treating RCC. The findings of this study support the continued flexibility in speech-language pathology service delivery to include in-person and telehealth platforms for RCC beyond the COVID-19 pandemic.