Novel data analytics tools combined with high-resolution cervical auscultation are needed to instrumentally screen for dysphagia

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Dysphagia (swallowing disorders), the most common issue associated with aging and neurological disorders, affects the daily lives of millions of Canadians. Dysphagia risk is currently assessed via screening, before the diagnostic gold-standard videofluoroscopic test, but many patients who silently aspirate pass initial screens. The PI's long-term research goal is to utilize computational approaches and instrumentation and translate innovative engineering research to clinical solutions for dysphagia. Therefore, the short-term research goal of this proposal is to combine the advances in fundamentally new data analytics tools with high-resolution cervical auscultation (HRCA - accelerometer and microphone recordings from the neck) to instrumentally screen for dysphagia. The educational objective is to create interdisciplinary training opportunities for highly qualified personnel (HQP) by combining signal processing, machine learning, and instrumentation. HQP will also acquire extensive knowledge of major health issues associated with aging and neurological disorders, which are the major contributors to healthcare expenditures in Canada. The proposed program is a major departure from the current signal processing efforts by focusing on the innovation of signal processing and machine learning approaches for dysphagia. Our transformative approach addresses current signal processing obstacles: First, classical approaches cannot translate HRCA signal analysis results to a validated clinical measure of swallowing impairment. To resolve this major issue, we will innovate new deep learning approaches based on convolutional and recursive neural networks along with time-frequency representations of HRCA signals. Second, inverse modeling of the swallowing function is unfeasible with traditional signal processing methods. To address this issue, our new approach is based on generative adversarial networks applied to signals. The key transformative aspect of the proposed research consists of fundamental theoretical advancements of data analytics tools for swallowing difficulties, while translating the research into clinically applicable tools, but also tools applicable in other engineering (e.g., artificial intelligence) and clinical fields (e.g., electrophysiology). This proposal supports the NSERC 2020 strategic plan by developing data-driven healthcare approaches and interdisciplinary training opportunities focused on the cultivation of HQP's creativity, translational, and communication skills. Furthermore, the PI will also actively recruit and encourage participation of women and underrepresented minority HQP to the proposed project by expanding currently available opportunities at the University of Toronto. Lastly, the PI's close collaboration with clinical partners offers the opportunity to transfer academic results into mainstream clinical practices via research publications, tutorials, workshops, clinical grand rounds, and technology transfer activities.