Improving Non-Invasive Aspiration Detection With Auxiliary Classifier Wasserstein Generative Adversarial Networks

Shu, Kechen; Mao, Shitong; Coyle, James L.; Sejdić, Ervin

doi:10.1109/jbhi.2021.3106565

Cited by 10 publications

(25 citation statements)

References 45 publications

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“…Recent technological advancements enable noninvasive detection of swallowing kinematics solely based on swallowing sounds and vibrations. [41][42][43][44][45][46] Other computer vision and artificial intelligence techniques were applied for automated frame-by-frame hyoid 47 and laryngeal 48 analyses on VFSS. These extracted measurements can be incorporated to our temporal understanding of laryngeal kinematics to estimate the risk of penetration or aspiration.…”

Section: Discussionmentioning

confidence: 99%

“…However, the objective measurements are often time‐consuming leading clinicians to form subjective inferences about aspiration risk and outcomes when they do not overtly occur during examination. Recent technological advancements enable noninvasive detection of swallowing kinematics solely based on swallowing sounds and vibrations 41–46 . Other computer vision and artificial intelligence techniques were applied for automated frame‐by‐frame hyoid 47 and laryngeal 48 analyses on VFSS.…”

Section: Discussionmentioning

confidence: 99%

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Temporal Sequence of Laryngeal Vestibule Closure and Reopening is Associated With Airway Protection

et al. 2022

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Background Upper esophageal sphincter opening (UESO), and laryngeal vestibule closure (LVC) are two essential kinematic events whose timings are crucial for adequate bolus clearance and airway protection during swallowing. Their temporal characteristics can be quantified through time‐consuming analysis of videofluoroscopic swallow studies (VFSS). Objectives We sought to establish a model to predict the odds of penetration or aspiration during swallowing based on 15 temporal factors of UES and laryngeal vestibule kinematics. Methods Manual temporal measurements and ratings of penetration and aspiration were conducted on a videofluoroscopic dataset of 408 swallows from 99 patients. A generalized estimating equation model was deployed to analyze association between individual factors and the risk of penetration or aspiration. Results The results indicated that the latencies of laryngeal vestibular events and the time lapse between UESO onset and LVC were highly related to penetration or aspiration. The predictive model incorporating patient demographics and bolus presentation showed that delayed LVC by 0.1 s or delayed LVO by 1% of the swallow duration (average 0.018 s) was associated with a 17.19% and 2.68% increase in odds of airway invasion, respectively. Conclusion This predictive model provides insight into kinematic factors that underscore the interaction between the intricate timing of laryngeal kinematics and airway protection. Recent investigation in automatic noninvasive or videofluoroscopic detection of laryngeal kinematics would provide clinicians access to objective measurements not commonly quantified in VFSS. Consequently, the temporal and sequential understanding of these kinematics may interpret such measurements to an estimation of the risk of aspiration or penetration which would give rise to rapid computer‐assisted dysphagia diagnosis. Level of Evidence 2 Laryngoscope, 133:521–527, 2023

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Temporal Sequence of Laryngeal Vestibule Closure and Reopening is Associated With Airway Protection

et al. 2022

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“…Engineering and Computing (Sarraf Shirazi et al, 2012;Sarraf Shirazi et al, 2014), IEEE Transaction of Biomedical Engineering (Sejdic et al, 2013), IEEE Journal of Biomedical and Health Informatics (Shu et al, 2022), and Scientific Reports (Park et al, 2022) that spanned across the clinical, engineering, and interdisciplinary science fields.…”

Section: Applicability Concernsmentioning

confidence: 99%

“…They implemented a 5-level discrete wavelet decomposition using Daubechies 5 wavelets and high-passed the signal using a 4 th order Butterworth filter with a 1-Hz cutoff frequency. Sejdic et al (2013) and Shu et al (2022) applied dual-axial and tri-axial accelerometers, respectively, and placed them anterior to the cricoid cartilage, in addition to a microphone. Both studies bandpassed the signal from 0.1 Hz to 3 kHz.…”

Section: Instruments and Testing Proceduresmentioning

confidence: 99%

“…On the other hand, while Frakking et al (2022) made use of an omnidirectional condenser microphone (C417, AKG Acoustics, Vienna, Austria) in the form of a circular O-ring lateral to the cricoid cartilage, Park et al (2022) recorded the speaking sound using an iPad (with an embedded microphone) placed 20 cm in front of the participants' faces (Umayahara et al, 2018). The sampling frequency was 44.1 kHz and was band-passed between Regarding the protocol for measurement, most of them referred to the standard swallowing assessment procedures that fed food/ liquid with different consistencies or thicknesses (Lee et al, 2006;Lee et al, 2011;Merey et al, 2012;Frakking et al, 2022;Shu et al, 2022). Nevertheless, some studies determined the swallowing items by the speech pathologists (Sarraf Shirazi et al, 2012;Sarraf Shirazi et al, 2014).…”

Section: Instruments and Testing Proceduresmentioning

confidence: 99%

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Computer-aided screening of aspiration risks in dysphagia with wearable technology: a Systematic Review and meta-analysis on test accuracy

Lai

Cheng

Lim

et al. 2023

Front. Bioeng. Biotechnol.

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Aspiration caused by dysphagia is a prevalent problem that causes serious health consequences and even death. Traditional diagnostic instruments could induce pain, discomfort, nausea, and radiation exposure. The emergence of wearable technology with computer-aided screening might facilitate continuous or frequent assessments to prompt early and effective management. The objectives of this review are to summarize these systems to identify aspiration risks in dysphagic individuals and inquire about their accuracy. Two authors independently searched electronic databases, including CINAHL, Embase, IEEE Xplore® Digital Library, PubMed, Scopus, and Web of Science (PROSPERO reference number: CRD42023408960). The risk of bias and applicability were assessed using QUADAS-2. Nine (n = 9) articles applied accelerometers and/or acoustic devices to identify aspiration risks in patients with neurodegenerative problems (e.g., dementia, Alzheimer’s disease), neurogenic problems (e.g., stroke, brain injury), in addition to some children with congenital abnormalities, using videofluoroscopic swallowing study (VFSS) or fiberoptic endoscopic evaluation of swallowing (FEES) as the reference standard. All studies employed a traditional machine learning approach with a feature extraction process. Support vector machine (SVM) was the most famous machine learning model used. A meta-analysis was conducted to evaluate the classification accuracy and identify risky swallows. Nevertheless, we decided not to conclude the meta-analysis findings (pooled diagnostic odds ratio: 21.5, 95% CI, 2.7–173.6) because studies had unique methodological characteristics and major differences in the set of parameters/thresholds, in addition to the substantial heterogeneity and variations, with sensitivity levels ranging from 21.7% to 90.0% between studies. Small sample sizes could be a critical problem in existing studies (median = 34.5, range 18–449), especially for machine learning models. Only two out of the nine studies had an optimized model with sensitivity over 90%. There is a need to enlarge the sample size for better generalizability and optimize signal processing, segmentation, feature extraction, classifiers, and their combinations to improve the assessment performance.Systematic Review Registration: (https://www.crd.york.ac.uk/prospero/), identifier (CRD42023408960).

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

Shin,

Lee,

Kwon

et al. 2024

Advanced Science

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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.

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Improving Non-Invasive Aspiration Detection With Auxiliary Classifier Wasserstein Generative Adversarial Networks

Cited by 10 publications

References 45 publications

Temporal Sequence of Laryngeal Vestibule Closure and Reopening is Associated With Airway Protection

Temporal Sequence of Laryngeal Vestibule Closure and Reopening is Associated With Airway Protection

Computer-aided screening of aspiration risks in dysphagia with wearable technology: a Systematic Review and meta-analysis on test accuracy

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

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