ArticleIEEE transactions on human-machine systems2015
Dysphagia Screening: Contributions of Cervical Auscultation Signals and Modern Signal-Processing Techniques.
Article in IEEE transactions on human-machine systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04468191 (Quantifying Fatigue of the Respiratory and Swallowing Musculature in Patients With Amyotrophic Lateral Sclerosis), which is not on this map. Cited by 45 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Quantifying Fatigue of the Respiratory and Swallowing Musculature in Patients With Amyotrophic Lateral Sclerosis
Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Dental Factors Associated With Oropharyngeal Dysphagia in Institutionalised Older Adults: A Systematic Review.Gerodontology · 2026Pooled it
- Technology to remotely capture key elements of swallowing and speech: State of the science in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Artificial intelligence tools for the assessment and management of dysphagia: a scoping review.BMJ open · 2026Article
- Acoustic Parameters of Swallowing in Adults and Older Adults: A Cross-Sectional Study.Dysphagia · 2026Article
- Multimodal LLM-driven IoT digital healthcare platform for intelligent dysphagia dietary monitoring.Frontiers in nutrition · 2026Article
- Parameter analysis using swallowing sounds shows differences in bolus volume, bolus viscosity, sex, and age.Scientific reports · 2025Article
- Comment on Chatwin et al. Waves of Precision: A Practical Guide for Reviewing New Tools to Evaluate Mechanical In-Exsufflation Efficacy in Neuromuscular Disorders.Journal of clinical medicine · 2024Article
- Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Feasibility of Oral Function Evaluation According to Dementia Severity in Older Adults with Alzheimer's Disease.Nutrients · 2024Article
- Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients.IEEE journal of translational engineering in health and medicine · 2024Article
- Review on the Advancements of Stethoscope Types in Chest Auscultation.Diagnostics (Basel, Switzerland) · 2023Review
- Pilot Study: Magnetic Motion Analysis for Swallowing Detection Using MEMS Cantilever Actuators.Sensors (Basel, Switzerland) · 2023Article
- A Comprehensive Assessment Protocol for Swallowing (CAPS): Paving the Way towards Computer-Aided Dysphagia Screening.International journal of environmental research and public health · 2023Article
- Autonomous Swallow Segment Extraction Using Deep Learning in Neck-Sensor Vibratory Signals From Patients With Dysphagia.IEEE journal of biomedical and health informatics · 2023Article
- Swallow Detection with Acoustics and Accelerometric-Based Wearable Technology: A Scoping Review.International journal of environmental research and public health · 2022Article
- Synchronization between videofluoroscopic swallowing study and surface electromyography in patients with neurological involvement presenting symptoms of dysphagiaBiomedica : revista del Instituto Nacional de Salud · 2022Article
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- Improving Non-Invasive Aspiration Detection With Auxiliary Classifier Wasserstein Generative Adversarial Networks.IEEE journal of biomedical and health informatics · 2022Article
- Real-time assessment of swallowing sound using an electronic stethoscope and an artificial intelligence system.Clinical and experimental dental research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Cervical auscultation is the recording of sounds and vibrations caused by the human body from the throat during swallowing. While traditionally done by a trained clinician with a stethoscope, much work has been put towards developing more sensitive and clinically useful methods to characterize the data obtained with this technique. The eventual goal of the field is to improve the effectiveness of screening algorithms designed to predict the risk that swallowing disorders pose to individual patients' health and safety. This paper provides an overview of these signal processing techniques and summarizes recent advances made with digital transducers in hopes of organizing the highly varied research on cervical auscultation. It investigates where on the body these transducers are placed in order to record a signal as well as the collection of analog and digital filtering techniques used to further improve the signal quality. It also presents the wide array of methods and features used to characterize these signals, ranging from simply counting the number of swallows that occur over a period of time to calculating various descriptive features in the time, frequency, and phase space domains. Finally, this paper presents the algorithms that have been used to classify this data into 'normal' and 'abnormal' categories. Both linear as well as non-linear techniques are presented in this regard.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.