Classification of pediatric snoring episodes using deep convolutional neural networks

In this paper, a deep convolutional neural network is designed for automatic detection of snoring episodes of children from ambient audio data. In order to test the performance of the network, a dataset collected from 38 pediatric patients with simple snoring or obstructive sleep apnea. The ambient audio signal is converted to Mel spectrogram in order to make the signal two dimensional. The network is trained using Adadelta algorithm on 1.200 samples. In order to fine tune the system parameters, a validation set of 300 samples is used. Finally, a test set of 300 samples was used observe the performance of the developed network. The samples in the training, validation, and test samples are all exclusive. The mean accuracy of the proposed system over 20 experiments is observed as 91% for snore and non-snore classification.