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1. Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings

   https://doi.org/10.1038/s41598-020-65492-1  

   Khalifa, Yassin; Coyle, James L.; Sejdić, Ervin (December 2020, Scientific Reports)

   Abstract High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening and aspiration detection, as it does not involve the use of harmful ionizing radiation approaches. Automatic extraction of swallowing events in cervical auscultation is a key step for swallowing analysis to be clinically effective. Using time-varying spectral estimation of swallowing signals and deep feed forward neural networks, we propose an automatic segmentation algorithm for swallowing accelerometry and sounds that works directly on the raw swallowing signals in an online fashion. The algorithm was validated qualitatively and quantitatively using the swallowing data collected from 248 patients, yielding over 3000 swallows manually labeled by experienced speech language pathologists. With a detection accuracy that exceeded 95%, the algorithm has shown superior performance in comparison to the existing algorithms and demonstrated its generalizability when tested over 76 completely unseen swallows from a different population. The proposed method is not only of great importance to any subsequent swallowing signal analysis steps, but also provides an evidence that such signals can capture the physiological signature of the swallowing process. 

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2. Wearable, epidermal devices for assessment of swallowing function

   https://doi.org/10.1038/s41528-023-00286-9  

   Rafeedi, Tarek; Abdal, Abdulhameed; Polat, Beril; Hutcheson, Katherine A; Shinn, Eileen H; Lipomi, Darren J (December 2023, npj Flexible Electronics)

   Abstract Swallowing is an ensemble of voluntary and autonomic processes key to maintaining our body’s homeostatic balance. Abnormal swallowing (dysphagia) can cause dehydration, malnutrition, aspiration pneumonia, weight loss, anxiety, or even mortality—especially in older adults—by airway obstruction. To prevent or mitigate these outcomes, it is imperative to regularly assess swallowing ability in those who are at risk of developing dysphagia and those already diagnosed with it. However, current diagnostic tools such as endoscopy, manometry, and videofluoroscopy require access to clinical experts to interpret the results. These results are often sampled from a limited examination timeframe of swallowing activity in a controlled environment. Additionally, there is some risk of periprocedural complications associated with these methods. In contrast, the field of epidermal sensors is finding non-invasive and minimally obtrusive ways to examine swallowing function and dysfunction. In this review, we summarize the current state of wearable devices that are aimed at monitoring swallowing function and detecting its abnormalities. We pay particular attention to the materials and design parameters that enable their operation. We examine a compilation of both proof-of-concept studies (which focus mainly on the engineering of the device) and studies whose aims are biomedical (which may involve larger cohorts of subjects, including patients). Furthermore, we briefly discuss the methods of signal acquisition and device assessment in relevant wearable sensors. Finally, we examine the need to increase adherence and engagement of patients with such devices and discuss enhancements to the design of such epidermal sensors that may encourage greater enthusiasm for at-home and long-term monitoring. 

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3. From SARS-CoV-2 infection to COVID-19 morbidity: an in silico projection of virion flow rates to the lower airway via nasopharyngeal fluid boluses

   https://doi.org/10.4193/RHINOL/21.053  

   Basu, S.; Akash, M.M.H.; Hochberg, N.S.; Senior, B.A.; Joseph-McCarthy, D.; Chakravarty, A. (January 2022, Rhinology Online)

   Background: While the nasopharynx is initially the dominant upper airway infection site for SARS-CoV-2, the physiologic mechanism launching the infection at the lower airway is still not well-understood. Based on the rapidity of infection progression to the lungs, it has been hypothesized that the nasopharynx may be acting as the primary seeding zone for subsequent contamination of the lower airway via aspiration of virus-laden boluses of nasopharyngeal fluids. Methodology: To examine the plausibility of the aspiration-driven mechanism, we have computationally tracked the inhalation process in three anatomic airway reconstructions and have quantified the nasopharyngeal liquid volume transmitted to the lower airspace during each aspiration. Results: Extending the numerical trends on aspiration volume to earlier records on aspiration frequencies indicates a total aspirated nasopharyngeal liquid volume of 0.3 – 0.76 ml/day. Subsequently, for mean sputum viral load, our modeling projects that the number of virions reaching the lower airway will range over 2.1×106 – 5.3×106 /day; for peak viral load, the corresponding number hovers between 7.1×108 – 1.8×109. Conclusions: The virion transmission findings fill in a key piece of the mechanistic puzzle on the systemic progression of SARS-CoV-2, and subjectively point to health conditions like dysphagia, with proclivity to increased aspiration, as some of the potential underlying risk factors for aggressive lung infections. 

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4. Automatic Estimation of Laryngeal Vestibule Closure Duration Using High-Resolution Cervical Auscultation Signals

   https://doi.org/10.1044/2020_PERSP-20-00073  

   Sabry, Aliaa; Mahoney, Amanda S.; Mao, Shitong; Khalifa, Yassin; Sejdić, Ervin; Coyle, James L. (December 2020, Perspectives of the ASHA Special Interest Groups)

   null (Ed.)

   Purpose Safe swallowing requires adequate protection of the airway to prevent swallowed materials from entering the trachea or lungs (i.e., aspiration). Laryngeal vestibule closure (LVC) is the first line of defense against swallowed materials entering the airway. Absent LVC or mistimed/shortened closure duration can lead to aspiration, adverse medical consequences, and even death. LVC mechanisms can be judged commonly through the videofluoroscopic swallowing study; however, this type of instrumentation exposes patients to radiation and is not available or acceptable to all patients. There is growing interest in noninvasive methods to assess/monitor swallow physiology. In this study, we hypothesized that our noninvasive sensor-based system, which has been shown to accurately track hyoid displacement and upper esophageal sphincter opening duration during swallowing, could predict laryngeal vestibule status, including the onset of LVC and the onset of laryngeal vestibule reopening, in real time and estimate the closure duration with a comparable degree of accuracy as trained human raters. Method The sensor-based system used in this study is high-resolution cervical auscultation (HRCA). Advanced machine learning techniques enable HRCA signal analysis through feature extraction and complex algorithms. A deep learning model was developed with a data set of 588 swallows from 120 patients with suspected dysphagia and further tested on 45 swallows from 16 healthy participants. Results The new technique achieved an overall mean accuracy of 74.90% and 75.48% for the two data sets, respectively, in distinguishing LVC status. Closure duration ratios between automated and gold-standard human judgment of LVC duration were 1.13 for the patient data set and 0.93 for the healthy participant data set. Conclusions This study found that HRCA signal analysis using advanced machine learning techniques can effectively predict laryngeal vestibule status (closure or opening) and further estimate LVC duration. HRCA is potentially a noninvasive tool to estimate LVC duration for diagnostic and biofeedback purposes without X-ray imaging. 

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5. Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing

   https://doi.org/10.1109/IEEECONF56349.2022.10052044  

   Ung, Brandon W.; Ge, Lulu; Parhi, Keshab K. (October 2022, Proc. 2022 Asilomar Conference on Signals, Systems and Computers)

   Hyperdimensional computing (HD) is an emerging brain-inspired paradigm used for machine learning classification tasks. It manipulates ultra-long vectors-hypervectors- using simple operations, which allows for fast learning, energy efficiency, noise tolerance, and a highly parallel distributed framework. HD computing has shown a significant promise in the area of biological signal classification. This paper addresses group-specific premature ventricular contraction (PVC) beat detection with HD computing using the data from the MIT-BIH arrhythmia database. Temporal, heart rate variability (HRV), and spectral features are extracted, and minimal redundancy maximum relevance (mRMR) is used to rank and select features for classification. Three encoding approaches are explored for mapping the features into the HD space. The HD computing classifiers can achieve a PVC beat detection accuracy of 97.7 % accuracy, compared to 99.4% achieved by more computationally complex methods such as convolutional neural networks (CNNs). 

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