A novel automated detection system for swallowing sounds during eating and speech under everyday conditions.

The wave analysis of swallowing sounds has been receiving attention because the recording process is easy and non-invasive. However, up until now, an expert has been needed to visually examine the entire recorded wave to distinguish swallowing from other sounds. The purpose of this study was to establish a methodology to automatically distinguish the sound of swallowing from sound data recorded during a meal in the presence of everyday ambient sound. Seven healthy participants (mean age: 26·7 ± 1·3 years) participated in this study. A laryngeal microphone and a condenser microphone attached to the nostril were used for simultaneous recording. Recoding took place while participants were taking a meal and talking with a conversational partner. Participants were instructed to step on a foot pedal trigger switch when they swallowed, representing self-enumeration of swallowing, and also to achieve six additional noise-making tasks during the meal in a randomised manner. The automated analysis system correctly detected 342 out of the 352 self-enumerated swallowing events (sensitivity: 97·2%) and 479 out of the 503 semblable wave periods of swallowing (specificity: 95·2%). In this study, the automated detection system for swallowing sounds using a nostril microphone was able to detect the swallowing event with high sensitivity and specificity even under the conditions of daily life, thus showing potential utility in the diagnosis or screening of dysphagic patients in future studies.

Image analysis of food particles can discriminate deficient mastication of mixed foodstuffs simulating daily meal.

Because food texture is regarded as an important factor for smooth deglutition, identification of objective parameters that could provide a basis for food texture selection for elderly or dysphagic patients is of great importance. We aimed to develop an objective evaluation method of mastication using a mixed test food comprising foodstuffs, simulating daily dietary life. The particle size distribution (>2 mm in diameter) in a bolus was analysed using a digital image under dark-field illumination. Ten female participants (mean age ± s.d., 27·6 ± 2·6 years) masticated a mixed test food comprising prescribed amounts of rice, sausage, hard omelette, raw cabbage and raw cucumber with 100%, 75%, 50% and 25% of the number of their masticatory strokes. A single set of coefficient thresholds of 0·10 for the homogeneity index and 1·62 for the particle size index showed excellent discrimination of deficient masticatory conditions with high sensitivity (0·90) and specificity (0·77). Based on the results of this study, normal mastication was discriminated from deficient masticatory conditions using a large particle analysis of mixed foodstuffs, thus showing the possibility of future application of this method for objective decision-making regarding the properties of meals served to dysphagic patients.