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. 2015 Nov 25:3:2900310.
doi: 10.1109/JTEHM.2015.2500562. eCollection 2015.

Smartphone-Based Real-time Assessment of Swallowing Ability From the Swallowing Sound

Dushyantha Jayatilake, Tomoyuki Ueno, Yohei Teramoto, Kei Nakai, Kikue Hidaka, Satoshi Ayuzawa, Kiyoshi Eguchi, Akira Matsumura, Kenji Suzuki

Smartphone-Based Real-time Assessment of Swallowing Ability From the Swallowing Sound

Dushyantha Jayatilake et al. IEEE J Transl Eng Health Med. .

Abstract

Dysphagia can cause serious challenges to both physical and mental health. Aspiration due to dysphagia is a major health risk that could cause pneumonia and even death. The videofluoroscopic swallow study (VFSS), which is considered the gold standard for the diagnosis of dysphagia, is not widely available, expensive and causes exposure to radiation. The screening tests used for dysphagia need to be carried out by trained staff, and the evaluations are usually non-quantifiable. This paper investigates the development of the Swallowscope, a smartphone-based device and a feasible real-time swallowing sound-processing algorithm for the automatic screening, quantitative evaluation, and the visualisation of swallowing ability. The device can be used during activities of daily life with minimal intervention, making it potentially more capable of capturing aspirations and risky swallow patterns through the continuous monitoring. It also consists of a cloud-based system for the server-side analyzing and automatic sharing of the swallowing sound. The real-time algorithm we developed for the detection of dry and water swallows is based on a template matching approach. We analyzed the wavelet transformation-based spectral characteristics and the temporal characteristics of simultaneous synchronised VFSS and swallowing sound recordings of 25% barium mixed 3-ml water swallows of 70 subjects and the dry or saliva swallowing sound of 15 healthy subjects to establish the parameters of the template. With this algorithm, we achieved an overall detection accuracy of 79.3% (standard error: 4.2%) for the 92 water swallows; and a precision of 83.7% (range: 66.6%-100%) and a recall of 93.9% (range: 72.7%-100%) for the 71 episodes of dry swallows.

Keywords: Dysphagia; Swallowing sound; bedside monitoring; cervical auscultation; mHealth; realtime monitoring; screening; videofluoroscopy.

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Figures

FIGURE 1.
FIGURE 1.
The Swallowscope for the continuous monitoring of swallowing activities at the bedside and during activities of daily life.
FIGURE 2.
FIGURE 2.
Setup for simultaneous VFSS and swallow sound capturing.
FIGURE 3.
FIGURE 3.
Microphone placement at the C5 level. Mild pressure from the spring characteristics of the neckband creates good contact between the skin and the microphone. The VF image shows a lateral view.
FIGURE 4.
FIGURE 4.
Comparison of the swallowing sound with the corresponding videofluoroscopic images. It is possible to couple the swallowing sound with a chronological series of anatomical events. (a) Resting (b) Larynx and soft palate ascend while the bolus entering the pharynx. Nasal passage is closed (c) Movement of the bolus through the UES. (d) Larynx return.
FIGURE 5.
FIGURE 5.
Audio and spectrogram characteristics of 4 different swallowing related events. (a) Swallow. (b) Cough. (c) Heavy breathing/gasping. (d) Clearing throat.
FIGURE 6.
FIGURE 6.
Variation of number of swallows during RSST of 15 healthy subjects. Usually the swallow count during 30 s period is less than 10.
FIGURE 7.
FIGURE 7.
Variation of sound-based average length of a swallow during RSST of 15 healthy subjects.
FIGURE 8.
FIGURE 8.
Realtime detection of swallowing sound. Segments are associated with different portions of the swallowing process.
FIGURE 9.
FIGURE 9.
Overview of the Swallowscope, showing the indicator side of the neck-worn microphone and the display of the smartphone application.
FIGURE 10.
FIGURE 10.
An example of realtime recognition of swallowing activities during RSST.
FIGURE 11.
FIGURE 11.
Comparison of RSST timing characteristics of 3 healthy subjects and 3 subjects with swallowing difficulties.
FIGURE 12.
FIGURE 12.
Automatic detection of swallowing activities compared against synchronised VF evidence.
See this image and copyright information in PMC

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