Obstructive Sleep Apnea identification based on VGGish networks

Sleep disorders are continuously growing in the population and can have a significant negative impact on everyday life. Economic and non-invasive systems able to support the diagnosis procedure will be more and more adopted in the next years. The aim of this work is to investigate the classification performance of a convolutional neural network, based on a VGG structure, to identify obstructive sleep apnea events. A recently developed dataset containing audio signals recorded from high-quality contact microphones placed on the trachea of the subjects under study has been adopted to perform transfer learning over a pre-trained VGGish network. Spectrogram images have been extracted from the audio signals to serve as inputs for the classification process. The importance of the time window selection has been also investigated and comparisons with other recent methods proposed in the literature are reported.