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Found 45 papers, 23 papers with code
Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG
In the context of cardiovascular diseases (CVD) that exhibit an elevated prevalence and mortality, the electrocardiogram (ECG) is a popular and standard diagnostic tool for doctors, commonly utilizing a 12-lead configuration in clinical practice.
Annotation of Sleep Depth Index with Scalable Deep Learning Yields Novel Digital Biomarkers for Sleep Health
Traditional sleep staging categorizes sleep and wakefulness into five coarse-grained classes, overlooking subtle variations within each stage.
Deep Imbalanced Regression to Estimate Vascular Age from PPG Data: a Novel Digital Biomarker for Cardiovascular Health
Photoplethysmography (PPG) is emerging as a crucial tool for monitoring human hemodynamics, with recent studies highlighting its potential in assessing vascular aging through deep learning.
Deep Learning for Detecting and Early Predicting Chronic Obstructive Pulmonary Disease from Spirogram Time Series: A UK Biobank Study
However, these methods fail to early predict an individual's probability of COPD in the future based on subtle features in the spirogram.
Review of Data-centric Time Series Analysis from Sample, Feature, and Period
Data is essential to performing time series analysis utilizing machine learning approaches, whether for classic models or today's large language models.
A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm
By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0. 7591.
Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data
This kind of continuous monitoring, in contrast to the short-term one, collects an extended period of fetal heart data.
A Review of Deep Learning Methods for Photoplethysmography Data
In this review, we systematically reviewed papers that applied deep learning models to process PPG data between January 1st of 2017 and July 31st of 2023 from Google Scholar, PubMed and Dimensions.
Improving Diffusion-Based Image Synthesis with Context Prediction
In this way, each point can better reconstruct itself by preserving its semantic connections with neighborhood context.
Ranked #1 on
Image Inpainting
on CelebA
(LPIPS metric)
Curricular and Cyclical Loss for Time Series Learning Strategy
Experiments on 3 kinds of tasks and 5 real-world datasets show the benefits of CRUCIAL for most deep learning models when learning time series.
Curriculum Design Helps Spiking Neural Networks to Classify Time Series
Spiking Neural Networks (SNNs) have a greater potential for modeling time series data than Artificial Neural Networks (ANNs), due to their inherent neuron dynamics and low energy consumption.
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
Given the lack of data, limited resources, semantic context requirements, and so on, this work focuses on TS-for-LLM, where we aim to activate LLM's ability for TS data by designing a TS embedding method suitable for LLM.
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation.
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization
To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings.
Frozen Language Model Helps ECG Zero-Shot Learning
In downstream classification tasks, METS achieves around 10% improvement in performance without using any annotated data via zero-shot classification, compared to other supervised and SSL baselines that rely on annotated data.
Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images
In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images.
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
In this paper, we study two types of bad positive pairs that can impair the quality of time series representation learned through contrastive learning: the noisy positive pair and the faulty positive pair.
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and their relations in images.
Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks
Continuous diagnosis and prognosis are essential for intensive care patients.
Diffusion Models: A Comprehensive Survey of Methods and Applications
This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration.
Confidence-Guided Learning Process for Continuous Classification of Time Series
In the real world, the class of a time series is usually labeled at the final time, but many applications require to classify time series at every time point.
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
Unsupervised/self-supervised graph representation learning is critical for downstream node- and graph-level classification tasks.
Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer
In this paper, we aim at learning representations for time series from a new perspective and propose Cross Reconstruction Transformer (CRT) to solve the aforementioned problems in a unified way.
Spatial Autoregressive Coding for Graph Neural Recommendation
To adequately leverage neighbor proximity and high-order information, we design a novel spatial autoregressive paradigm.
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training
The results show that our method performed more effectively against adversarial attacks targeting on ECG classification than the other baseline methods, namely, adversarial training, defensive distillation, Jacob regularization, and noise-to-signal ratio regularization.
A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings
Finally, with the help of a clinician, we conduct case studies to explain the results of large uncertainties and incorrect predictions with small uncertainties.
A Simple Self-Supervised ECG Representation Learning Method via Manipulated Temporal-Spatial Reverse Detection
To verify the effectiveness of the proposed method, we perform a downstream task to detect atrial fibrillation (AF) which is one of the most common ECG tasks.
MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs
MAML is expected to better transfer the knowledge from a large dataset and use only a few recordings to quickly adapt the model to a new person.
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations.
Spectral Propagation Graph Network for Few-shot Time Series Classification
To the best of our knowledge, SPGN is the first to utilize spectral comparisons in different intervals and involve spectral propagation across all time series with graph networks for few-shot TSC.
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series
First, we revisit the augmentation methods for time series of existing works and note that they mostly use segment-level augmentation derived from time slicing, which may bring about sampling bias and incorrect optimization with false negatives due to the loss of global context.
ACCTS: an Adaptive Model Training Policy for Continuous Classification of Time Series
For this demand, we propose a new concept, Continuous Classification of Time Series (CCTS), to achieve the high-accuracy classification at every time.
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals
In inter subject self-supervision, we design a set of data augmentations according to the clinical characteristics of cardiac signals and perform contrastive learning among subjects to learn distinctive representations for various types of patients.
GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization
Each local model is learned from the local data and aligns with its distribution for customization.
TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data
In this work, we propose a novel Time Encoding (TE) mechanism.
A Review of Designs and Applications of Echo State Networks
Echo State Network (ESN) is simple type of RNNs and has emerged in the last decade as an alternative to gradient descent training based RNNs.
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management.
Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning
Time-series forecasting is one of the most active research topics in artificial intelligence.
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units
HOLMES is tested on risk prediction task on pediatric cardio ICU data with above 95% prediction accuracy and sub-second latency on 64-bed simulation.
CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram
Electrocardiogram (ECG) is one of the most convenient and non-invasive tools for monitoring peoples' heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al.
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e. g., intensive care units).
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
Methods:We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between Jan. 1st of 2010 and Feb. 29th of 2020 from Google Scholar, PubMed, and the DBLP.
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities.
RDPD: Rich Data Helps Poor Data via Imitation
In many situations, we need to build and deploy separate models in related environments with different data qualities.
ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks
We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification.
Ranked #1 on
Time Series Classification
on Physionet 2017 Atrial Fibrillation
(F1 (Hidden Test Set) metric)
