Neck sensor-supported hyoid bone movement tracking during swallowing

Abstract

Hyoid bone movement is an important physiological event during swallowing that contributes to normal swallowing function. In order to determine the adequate hyoid bone movement, clinicians conduct an X-ray videofluoroscopic swallowing study, which even though it is the gold-standard technique, has limitations such as radiation exposure and cost. Here, we demonstrated the ability to track the hyoid bone movement using a non-invasive accelerometry sensor attached to the surface of the human neck. Specifically, deep neural networks were used to mathematically describe the relationship between hyoid bone movement and sensor signals. Training and validation of the system were conducted on a dataset of 400 swallows from 114 patients. Our experiments indicated the computer-aided hyoid bone movement prediction has a promising performance when compared with human experts’ judgements, revealing that the universal pattern of the hyoid bone movement is acquirable by the highly nonlinear algorithm. Such a sensor-supported strategy offers an alternative and widely available method for online hyoid bone movement tracking without any radiation side-effects and provides a pronounced and flexible approach for identifying dysphagia and other swallowing disorders.

Artificial Intelligence and Dysphagia: Novel Solutions to Old Problems

“…We concurrently acquired vibration signals and videofluoroscopy images during the swallowing exam. We used the ground truth from the trained-human ratings of the videofluoroscopic images to develop a machine learning model that can infer about hyoid bone displacement from neck vibrations during swallowing (FIGURE 3) (5,6) . Trained raters marked the location of the hyoid bone on every frame of a swallow and this information was given to the computer model for training.…”

“…It was astonishing to witness the machine learning model accurately inferring about the hyoid bone displacement using only neck vibrations (no x-rays needed!) (5) .…”

Characterizing Effortful Swallows from Healthy Community Dwelling Adults Across the Lifespan Using High-Resolution Cervical Auscultation Signals and MBSImP Scores: A Preliminary Study

“…HRCA is a method of characterizing swallow function that integrates information from acoustic and vibratory signals from non-invasive sensors (contact microphone, tri-axial accelerometer) attached to the anterior laryngeal framework during swallowing. Following collection of HRCA signals, HRCA signal features are extracted using advanced signal processing techniques to use the HRCA signal features as input to machine learning algorithms to provide insight into swallowing physiology using human ratings of VF images as the “ground truth.” HRCA has demonstrated promise as a dysphagia screening method and potential diagnostic adjunct to VF by classifying safe and unsafe swallows (as measured by the penetration-aspiration scale) [ 11 – 17 ], tracking hyoid bone displacement in healthy adults and patients with suspected dysphagia [ 18 , 19 ], annotating temporal swallow kinematic events in healthy adults and patients with suspected dysphagia (e.g., durations of upper esophageal sphincter opening and laryngeal vestibule closure) [ 20 – 22 ], categorizing swallows between healthy participants and different patient populations [ 23 , 24 ], and detecting clinical ratings of swallow physiology in patients with suspected dysphagia using the Modified Barium Swallow Impairment Profile (MBSImP) [ 25 ] with a high degree of accuracy [ 19 , 21 ]. However, the utility of HRCA’s capabilities to noninvasively characterize these physiologic events, many of which are targets of behavioral augmentation via compensatory swallowing maneuvers (e.g., effortful swallow, Mendelsohn maneuver), and differentiate between swallows in which they are accurately deployed without imaging verification, has yet to be investigated.…”

Upper Esophageal Sphincter Opening Segmentation With Convolutional Recurrent Neural Networks in High Resolution Cervical Auscultation

“…HRCA combines the use of vibratory signals from an accelerometer with acoustic signals from a microphone attached to the anterior neck region during swallowing. Following collection of signals, advanced machine learning techniques are used to examine the association between HRCA signals and physiological events that occur during swallowing [31], [32].…”