Segmentation of dual-axis swallowing accelerometry signals in healthy subjects with analysis of anthropometric effects on duration of swallowing activities.
Dysphagia (swallowing difficulty) is a serious and debilitating condition that often accompanies stroke, acquired brain injury, and neurodegenerative illnesses. Individuals with dysphagia are prone to aspiration (the entry of foreign material into the airway), which directly increases the risk of serious respiratory consequences such as pneumonia. Swallowing accelerometry is a promising noninvasive tool for the detection of aspiration and the evaluation of swallowing. In this paper, dual-axis accelerometry was implemented since the motion of the hyolaryngeal complex occurs in both anterior-posterior and superior-inferior directions during swallowing. Dual-axis cervical accelerometry signals were acquired from 408 healthy subjects during dry, wet, and wet chin tuck swallowing tasks. The proposed segmentation algorithm is based on the idea of sequential fuzzy partitioning of the signal and is well suited for long signals with nonstationary variance. The algorithm was validated with simulated signals with known swallowing locations and a subset of 295 real swallows manually segmented by an experienced speech language pathologist. In both cases, the algorithm extracted individual swallows with over 90% accuracy. The time duration analysis was carried out with respect to gender, body mass index (BMI), and age. Demographic and anthropometric variables influenced the duration of these segmented signals. Male participants exhibited longer swallows than female participants (p=0.05). Older participants and participants with higher BMIs exhibited swallows with significantly longer (p=0.05) duration than younger participants and those with lower BMIs, respectively.
Sejdić
Ervin
E
Bloorview Research Institute and Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M4G 1R8, Canada. esejdic@ieee.org
Steele
Catriona M
CM
Chau
Tom
T
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D000886:Anthropometry / Q000379:methods*; D003679:Deglutition / Q000502:physiology*; D004576:Electromyography; D006801:Humans; D008875:Middle Aged; D008954:Models, Biological; D009068:Movement; D010363:Pattern Recognition, Automated; D012016:Reference Values; D013997:Time Factors; D055815:Young Adult