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Found 15 papers, 7 papers with code
L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging
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.
Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones
The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time.
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level.
Multi-view Audio and Music Classification
The learned embedding in the subnetworks are then concatenated to form the multi-view embedding for classification similar to a simple concatenation network.
Self-Attention Generative Adversarial Network for Speech Enhancement
Existing generative adversarial networks (GANs) for speech enhancement solely rely on the convolution operation, which may obscure temporal dependencies across the sequence input.
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
This work proposes a sequence-to-sequence sleep staging model, XSleepNet, that is capable of learning a joint representation from both raw signals and time-frequency images.
Ranked #1 on
Sleep Stage Detection
on PhysioNet Challenge 2018
Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization
We employ the pretrained SeqSleepNet (i. e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model.
Improving GANs for Speech Enhancement
The former constrains the generators to learn a common mapping that is iteratively applied at all enhancement stages and results in a small model footprint.
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database.
Ranked #1 on
Multimodal Sleep Stage Detection
on Surrey-PSG
Automatic Sleep Stage Classification
Multimodal Sleep Stage Detection
+2
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch
This work presents a deep transfer learning approach to overcome the channel mismatch problem and transfer knowledge from a large dataset to a small cohort to study automatic sleep staging with single-channel input.
Spatio-Temporal Attention Pooling for Audio Scene Classification
Acoustic scenes are rich and redundant in their content.
Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?
Moreover, as model fusion with deep network ensemble is prevalent in audio scene classification, we further study whether, and if so, when model fusion is necessary for this task.
Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events.
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling of sequential epochs.
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways.
Ranked #2 on
Sleep Stage Detection
on MASS SS2
