Differences in brain networks during consecutive swallows detected using an optimized vertex-frequency algorithm.

Patients with dysphagia can have higher risks of aspiration after repetitive swallowing activity due to the "fatigue effect". However, it is still unknown how consecutive swallows affect brain activity. Therefore, we sought to investigate differences in swallowing brain networks formed during consecutive swallows using a signal processing on graph approach. Data were collected from 55 healthy people using electroencephalography (EEG) signals. Participants performed dry swallows (i.e., saliva swallows) and wet swallows (i.e., water, nectar-thick, and honey thick swallows). After standard pre-processing of the EEG time series, brain networks were formed using the time-frequency-based synchrony measure, while signals on graphs were formed as a line graph of the brain networks. For calculating the vertex frequency information from the signals on graphs, the proposed algorithm was based on the optimized window size for calculating the windowed graph Fourier transform and the graph S-transform. The proposed algorithms were tested using synthetic signals and showed improved energy concentration in comparison to the original algorithm. When applied to EEG swallowing data, the optimized windowed graph Fourier transform and the optimized graph S-transform showed that differences exist in brain activity between consecutive swallows. In addition, the results showed higher differences between consecutive swallows for thicker liquids.