High-Resolution Cervical Auscultation Signal Features Reflect Vertical and Horizontal Displacements of the Hyoid Bone During Swallowing

Abstract

Millions of people across the globe suffer from swallowing difficulties, known as dysphagia, which can lead to malnutrition, pneumonia, and even death. Swallowing cervical auscultation, which has been suggested as a noninvasive screening method for dysphagia, has not been associated yet with any physical events. In this paper, we have compared the hyoid bone displacement extracted from the videofluoroscopy images of 31 swallows to the signal features extracted from the cervical auscultation recordings captured with a tri-axial accelerometer and a microphone. First, the vertical displacement of the anterior part of the hyoid bone is related to the entropy rate of the superior–inferior swallowing vibrations and to the kurtosis of the swallowing sounds. Second, the vertical displacement of the posterior part of the hyoid bone is related to the bandwidth of the medial–lateral swallowing vibrations. Third, the horizontal displacements of the posterior and anterior parts of the hyoid bone are related to the spectral centroid of the superior–inferior swallowing vibrations and to the peak frequency of the medial–lateral swallowing vibrations, respectively. At last, the airway protection scores and the command characteristics were associated with the vertical and horizontal displacements, respectively, of the posterior part of the hyoid bone. Additional associations between the patients’ characteristics and auscultations’ signals were also observed. The hyoid bone maximal displacement is a cause of swallowing vibrations and sounds. High-resolution cervical auscultation may offer a noninvasive alternative for dysphagia screening and additional diagnostic information.

Neck sensor-supported hyoid bone movement tracking during swallowing

“…Vibratory signals may be used as surrogates to imaging, tracking some physiological aspects of swallowing, such as hyoid bone movement, by placing surface sensors on the skin of a person's anterior neck [3,17,18]. Currently, there is growing but limited research suggesting the relationship between hyoid bone movement and tri-direction vibration signals, including the anterior-posterior (A-P), superior-interior (S-I) and medial-lateral (M-L) directions [16,[19][20][21]. In addition to this, no studies have investigated real-time tracking of hyoid bone movement using non-invasive tools, which has remained a difficult and unresolved problem for the last 20 years.…”

Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoring

“…Information is transformed into other modalities. Modality transformations within target systems are driven by internal mechanisms, particularly where the target information is stored or generated, and the pathways from which it may then emanate [50] , [69] (Ṁ, ①, ②). Two significant regions of modality transformation are within the target system, and beyond its surface.…”

“…Many solutions digitise an existing gold standard and consequently inherit structure and constraints from it [69] . Such solutions follow a typical pattern involving quantification of a biomarker using a particular modality and sensor, signal sensing leading to a measurement of the side-channel, deduction of a diagnosis, and verification of results relative to the gold standard ( section III-C .6) (Ṁ, ①, ②, ③, ④).…”

“…The utilisation of stand-alone sensors for side-channel sensing for MDM often indicates that the research is in the proof-of-concept and prototyping stages. With a combination of a small microphone and accelerometer placed externally on a patient’s throat, inference of the interior structure and movement of the cartilage and bones can be made, in turn classifying swallowing health [69] . Microwaves reflected off people has been shown to be finesse enough to identify those who may have a gait that exhibits the characteristics associated with shaking palsy (a defining symptom of Parkinson patients) [70] .…”

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

“…Particularly, we are interested in automated identification of vibratory and acoustic signals demarcating individual swallows using accelerometers and microphones 3 . Such automatic segmentation algorithms are critical for many applications that rely on swallowing sounds and vibrations which have been suggested as alternative bedside tools for dysphagia screening [10][11][12][13][14][15][16][17][18] , to discriminate between patients with healthy and dysphagic swallows 10,11 .Dysphagia is a swallowing disorder that frequently follows stroke, neurodegenerative diseases, head and neck cancer and head injuries among many other etiologies 19 . Swallowing physiology and kinematics can be monitored and evaluated through various diagnostic imaging tools like endoscopy and ultrasound, but the gold standard…”