Trend Statistics Network and Channel invariant EEG Network for sleep arousal study

Achuth Rao, MV and Kumar Ghosh, P and Bhattacharjee, T and Choudhury, AD (2019) Trend Statistics Network and Channel invariant EEG Network for sleep arousal study. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, 23-27 July 2019, Berlin, Germany, Germany, pp. 5716-5722.

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Abstract

Sleep is a very important part of life. Lack of sleep or sleep disorder can cause a negative impact on day to day life and can have long term serious consequences. In this work, we propose an end-to-end trainable neural network for automated sleep arousal scoring. The network consists of two main parts. Firstly, a trend statistics network computes the moving average of the filtered signals at different scales. Secondly, we propose a channel invariant EEG network to detect the arousals in any Electroencephalography (EEG) channel. Finally, we combine the features from various channels through a convolution network and a bi-directional long short-term memory to predict the probability of arousal. Further, we propose an objective function that uses only respiratory effort related arousal (RERA) and non-arousal regions to optimize the network. We also propose a method to estimate the respiratory disturbance index (RDI) from the probability predicted by the network. Evaluation on Physionet Challenge 2018 database shows that the proposed method detects RERA with mean area under the precision-recall curve (AUPRC) of 0.50 in a 10-fold cross validation setup. The mean absolute error of RDI prediction is 6.11, while a two-class RDI severity prediction yields a specificity of 75 and a sensitivity of 83.

Item Type:	Conference Paper
Publication:	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	Copyright of this article belongs to IEEE
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	13 Feb 2020 11:02
Last Modified:	13 Feb 2020 11:02
URI:	http://eprints.iisc.ac.in/id/eprint/64457

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