查看文章

A typical artificial pancreas depends only on the continuous glucose monitoring (CGM) value for insulin dosing. However, both the insulin infusion and the glucose sensing are subject to time delays and slow dynamics. An automated and reliable meal onset information could enhance the control outcome of artificial pancreas by making it possible to infuse insulin earlier and thereby avoid large postprandial glucose excursions. In this study we employ abdominal sounds recorded in two healthy volunteers with a condenser microphone and propose an automated approach for meal onset detection from abdominal sounds. We use the Mel-frequency cepstral coefficients (MFCCs) and wavelet entropy extracted from the abdominal sounds as features. These features are fed to a simple feed forward neural network for discriminating meal from no-meal abdominal sounds. This approach detects meal onset with an …