Search Results for author:
Found 11 papers, 4 papers with code
Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition
To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena.
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning
From the perspective of intelligent wearable applications, the possibility of a comprehensive ECG interpretation algorithm based on single-lead ECGs is also confirmed.
A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signals
To address this issue, we establish a structural causal model as the foundation to customize the intervention approaches on Am and Ar, respectively.
Neural network facilitated ab initio derivation of linear formula: A case study on formulating the relationship between DNA motifs and gene expression
We showed that this linear model could predict gene expression levels using promoter sequences with a performance comparable to deep neural network models.
Subject Envelope based Multitype Reconstruction Algorithm of Speech Samples of Parkinson's Disease
The results show that the proposed algorithm is effective.
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.
Ranked #1 on
Automatic Sleep Stage Classification
on Sleep-EDF
Classification of 12-lead ECGs: the PhysioNet/ Computing in Cardiology Challenge 2020
Main results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry.
Temporal-Framing Adaptive Network for Heart Sound Segmentation without Prior Knowledge of State Duration
Moreover, the TFAN-based method achieved an overall F1 score of 99. 2%, 94. 4%, 91. 4% on LEVEL-I, -II and -III data respectively, compared to 98. 4%, 88. 54% and 79. 80% for the current state-of-the-art method.
Active Stacking for Heart Rate Estimation
Moreover, active learning can be used to optimally select a few trials from a new subject to label, based on which a stacking ensemble regression model can be trained to aggregate the base estimators.
An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection
Over the past few decades, methods for classification and detection of rhythm or morphology abnormalities in ECG signals have been widely studied.
Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific Data
We demonstrate its high prediction accuracy and sensitivity through the task of predictive feature selection on a simulated dataset and the application of predicting open chromatin sites in the human genome.
