Automated recognition of sleep arousal using multimodal and personalized deep ensembles of neural networks

Background and Aim: Monitoring physiological signals during sleep can have substantial impact on detecting temporary intrusion of wakefulness, referred to as sleep arousals. To overcome the problems associated with the cubersome visual inspection of these events by experts, sleep arousal recognition algorithms have been proposed. Method: As part of the Physionet/Computing in Cardiology Challenge 2018, this study proposes a deep ensemble neural network architecture for automatic arousal recognition from multi-modal sensor signals. Separate branches of the neural network extract features from electro-encephalography, electrooculography, electromyogram, breathing patterns and oxygen saturation level; and a final fully-connected neural network combines features computed from the signal sources to estimate the probability of arousal in each region of interest. We investigate the use of shared-parameter Siamese architectures for effective feature calibration. Namely, at each forward and backward pass through the network we concatenate to the input a user-specific template signal that is processed by an identical copy of the network. Result: The proposed architecture obtains an AUPR score of 0.40 on the test set of the official phase of Physionet/CbiC Challenge 2018. A score of 0.45 is obtained by means of 10 -fold cross-validation on the training set

Automated Recognition of Sleep Arousal Using Multimodal and Personalized Deep Ensembles of Neural Networks

Background and Aim: Monitoring physiological signals during sleep can have substantial impact on detecting temporary intrusion of wakefulness, referred to as sleep arousals. To overcome the problems associated with the cubersome visual inspection of these events by experts, sleep arousal recognition algorithms have been proposed. Method: As part of the Physionet/Computing in Cardiology Challenge 2018, this study proposes a deep ensemble neural network architecture for automatic arousal recognition from multi-modal sensor signals. Separate branches of the neural network extract features from electro-encephalography, electrooculography, electromyogram, breathing patterns and oxygen saturation level; and a final fully-connected neural network combines features computed from the signal sources to estimate the probability of arousal in each region of interest. We investigate the use of shared-parameter Siamese architectures for effective feature calibration. Namely, at each forward and backward pass through the network we concatenate to the input a user-specific template signal that is processed by an identical copy of the network. Result: The proposed architecture obtains an AUPR score of 0.40 on the test set of the official phase of Physionet/CbiC Challenge 2018. A score of 0.45 is obtained by means of 10 -fold cross-validation on the training set