4 research outputs found
DNN Filter Bank Improves 1-Max Pooling CNN for Single-Channel EEG Automatic Sleep Stage Classification
We present in this paper an efficient convolutional neural network (CNN) running on time-frequency image features for automatic sleep stage classification. Opposing to deep architectures which have been used for the task, the proposed CNN is much simpler. However, the CNN’s convolutional layer is able to support convolutional kernels with different sizes, and therefore, capable of learning features at multiple temporal resolutions. In addition, the 1-max pooling strategy is employed at the pooling layer to better capture the shift-invariance property of EEG signals. We further propose a method to discriminatively learn a frequency-domain filter bank with a deep neural network (DNN) to preprocess the time-frequency image features. Our experiments show that the proposed 1-max pooling CNN performs comparably with the very deep CNNs in the literature on the Sleep-EDF dataset. Preprocessing the time-frequency image features with the learned filter bank before presenting them to the CNN leads to significant improvements on the classification accuracy, setting the state-of-the-art performance on the dataset
L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging
Human sleep is cyclical with a period of approximately 90 minutes, implying
long temporal dependency in the sleep data. Yet, exploring this long-term
dependency when developing sleep staging models has remained untouched. In this
work, we show that while encoding the logic of a whole sleep cycle is crucial
to improve sleep staging performance, the sequential modelling approach in
existing state-of-the-art deep learning models are inefficient for that
purpose. We thus introduce a method for efficient long sequence modelling and
propose a new deep learning model, L-SeqSleepNet, which takes into account
whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on
four distinct databases of various sizes, we demonstrate state-of-the-art
performance obtained by the model over three different EEG setups, including
scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear
EEG (cEEGrid), even with a single EEG channel input. Our analyses also show
that L-SeqSleepNet is able to alleviate the predominance of N2 sleep (the major
class in terms of classification) to bring down errors in other sleep stages.
Moreover the network becomes much more robust, meaning that for all subjects
where the baseline method had exceptionally poor performance, their performance
are improved significantly. Finally, the computation time only grows at a
sub-linear rate when the sequence length increases.Comment: 9 pages, 4 figures, updated affiliation