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Found 32 papers, 14 papers with code
Compressed Sensing of Multi-Channel EEG Signals: The Simultaneous Cosparsity and Low Rank Optimization
Significance: The proposed method enables successful compressed sensing of EEG signals even when the signals have no good sparse representation.
Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG
Similarly, the convolutional neural network scored 72. 1% on the augmented database and 83% on the test set.
Personalizing deep learning models for automatic sleep staging
Starting from a general convolutional neural network architecture, we allow the model to learn individual characteristics of the first night of sleep in order to quantify sleep stages of the second night.
Neurons and Cognition
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
A unifying Bayesian approach for preterm brain-age prediction that models EEG sleep transitions over age
Preterm newborns undergo various stresses that may materialize as learning problems at school-age.
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.
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.
Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis
This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation.
Spatio-Temporal Attention Pooling for Audio Scene Classification
Acoustic scenes are rich and redundant in their content.
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.
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
Screening for REM Sleep Behaviour Disorder with Minimal Sensors
This study investigates a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors.
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.
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.
Monitoring Depression in Bipolar Disorder using Circadian Measures from Smartphone Accelerometers
The objective of this study was to use acceleration data recorded from smartphones to predict levels of depression in a population of participants diagnosed with bipolar disorder.
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
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation
Detectable change points include abrupt changes in the slope, mean, variance, autocorrelation function and frequency spectrum.
Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach
To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.
Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones
The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic.
Light-weight sleep monitoring: electrode distance matters more than placement for automatic scoring
Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring.
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.
Feature matching as improved transfer learning technique for wearable EEG
This method consists of training a model with larger amounts of data from the source modality and few paired samples of source and target modality.
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.
Avoiding Post-Processing with Event-Based Detection in Biomedical Signals
Methods: We propose an event-based modeling framework that directly works with events as learning targets, stepping away from ad-hoc post-processing schemes to turn model outputs into events.
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.
Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography
This study proposes a framework for automated limb-movement detection by fusing data from two EMG sensors (from the left and right limb) through a Dirichlet process mixture model.
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
The results indicate that training the model on multimodal data does positively influence performance when tested on unimodal data.
Ranked #1 on
Sleep Stage Detection
on SHHS
U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging
As machine learning becomes increasingly prevalent in critical fields such as healthcare, ensuring the safety and reliability of machine learning systems becomes paramount.
Explaining the Model and Feature Dependencies by Decomposition of the Shapley Value
In this paper we examine this question and claim that they represent two different explanations which are valid for different end-users: one that explains the model and one that explains the model combined with the feature dependencies in the data.
Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions
Additionally we explore how a domain expert can provide feature attributions which are sufficiently correct to improve the model.
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital
The combination of wearable EEG and EMG achieved overall the most clinically useful performance in offline TCS detection with a sensitivity of 97. 7%, a FPR of 0. 4/24 h, a precision of 43. 0%, and a F1-score of 59. 7%.
