🚀 记录用于与罕见心脏疾病以及ECG可解释性相关paper
📢 动态更新中...
📘 罕见疾病预警：
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心脏骤停
- Developing neural network models for early detection of cardiac arrest in emergency department
- Development and validation of a deep-learning-based pediatric early warning system: A single-center study
- Real-time prediction of acute cardiovascular events using hardware-implemented Bayesian networks
- Early Prediction of Cardiac Arrest (Code Blue) using Electronic Medical Records
- Using Time Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit
- Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the ward
- Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS)
- Learning from different perspectives: robust cardiac arrest prediction via temporal transfer learning
- Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study
- Detecting patient deterioration using artificial intelligence in a rapid response system
- Machine learning based early detection system of cardiac arrest
- An intelligent warning model for early prediction of cardiac arrest in sepsis patients
- An energy efficient wearable smart IoT system to predict cardiac arrest
- Predicting cardiac arrest and respiratory failure using feasible artificial intelligence with simple trajectories of patient data据
- Prediction of cardiac arrest in intensive care patients through machine learning
- An algorithm based on deep learning for predicting in‐hospital cardiac arrest
- Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart（Nature'2022)
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AF误诊
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肥厚型心肌病（HCM）
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心脏淀粉样变
- Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms（Nature'2021）
- Detection of cardiac amyloidosis on electrocardiogram images using machine learning and deep learning techniques
- Artificial Intelligence–Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis
- Prediction of Transthyretin Cardiac Amyloidosis in Heart Failure With Preserved Ejection Fraction: Artificial Intelligence Electrocardiogram versus Traditional Risk Scores
- Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery
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预测死亡
- Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network（Nature'2020）
- Combined Echocardiographic Left Ventricular Hypertrophy and Electrocardiographic ST Depression Improve Prediction of Mortality in American Indians
- A Supervised Learning Approach for ICU Mortality Prediction based on Unstructured Electrocardiogram Text Reports
📰 可解释性：
- Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery
- Computer-Aided Decision Support System for Diagnosis of Heart Diseases
- Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification
- Explainable AI decision model for ECG data of cardiac disorders
- Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram
- Explainable Artificial Intelligence Model for Diagnosis of Atrial Fibrillation Using Holter Electrocardiogram Waveforms
- Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI
- Explainable Deep learning based Approach for Multi-label Classification of Electrocardiogram
- Towards explainable artificial intelligence and explanation user interfaces to open the ‘black box’ of automated ECG interpretation
- Classification of organised atrial arrythmias using explainable artificial intelligence
- Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals
- Arithmetic Optimization Algorithm with Explainable Artificial Intelligence Technique for Biomedical Signal Analysis
- Interpretable heartbeat classification using local model-agnostic explanations on ECGs
- Detection and classification of arrhythmia using an explainable deep learning model
- Deep neural networks learn by using human-selected electrocardiogram features and novel features
- Explainable AI meets Healthcare: A Study on Heart Disease Dataset
- Designing Theory-Driven User-Centric Explainable AI
- Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG (Nature'2020)
📘 Nature，TMI，MIA，MICCAI 心电相关文章：
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Nature
- Automatic diagnosis of the 12-lead ECG using a deep neural network（Nature Communication'2020）
- Deep neural network-estimated electrocardiographic age as a mortality predictor（Nature Communication'2021）
- Automated multilabel diagnosis on electrocardiographic images and signals（Nature Communication'2022）
- ECG data dependency for atrial fibrillation detection based on residual networks（Scientific Report'2021）
- Author Correction: Automatic diagnosis of the 12-lead ECG using a deep neural network（Nature Communication'2020）
- Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features（Scientific Report'2021）
- Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction（Scientific Report'2020）
- Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier（Scientific Report'2020）
- Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram（Nature Communication'2020）
- ECG-based machine-learning algorithms for heartbeat classification（Scientific Report'2021）
- transfer learning for ECG classification（Scientific Report'2021）
- A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions（Nature Communication'2021）
- Artificial intelligence-enhanced electrocardiography in cardiovascular disease management（Nature Review Cardiology'2021）
- Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG（Scientific Report'2020）
- Contactless facial video recording with deep learning models for the detection of atrial fibrillation（Scientific Report'2022）
- DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine（Scientific Report'2021）
- Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography（Scientific Report'2020）
- Transfer Learning in ECG Classification from Human to Horse Using a Novel Parallel Neural Network Architecture（Scientific Report'2020）
- Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO（Scientific Report'2017）
- Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset（Scientific Report'2022）
- Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network（Nature Medicine'2020）
- Accessory pathway analysis using a multimodal deep learning model（Scientific Report'2021）
- Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks（Machine Intelligence'2021）
- Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network（Nature Medicine'2019）
- Convolutional neural network for classification of eight types of arrhythmia using 2D time–frequency feature map from standard 12-lead electrocardiogram（Scientific Report'2021）
- Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning（Scientific Report'2021）
- Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram（Nature Medicine'2019）
- Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms（Scientific Report'2020）
- Explaining deep neural networks for knowledge discovery in electrocardiogram analysis（Scientific Report'2021）
- A deep transfer learning approach for wearable sleep stage classification with photoplethysmography（Digital Medicine'2021）
- Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data（Digital Medicine'2020）
- Deep learning models for electrocardiograms are susceptible to adversarial attack（Nature Medicine'2020）
- Concatenated convolutional neural network model for cuffless blood pressure estimation using fuzzy recurrence properties of photoplethysmogram signals（Scientific Report'2022）
- Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization（Scientific Report'2021）
- Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management（Nature Review Cardiology'2020）
- A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm（Scientific Report'2021）
- Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias（Scientific Report'2017）
- Deep Feature Learning for Sudden Cardiac Arrest Detection in Automated External Defibrillators（Scientific Report'2018）
- Artificial intelligence to improve the diagnosis of cardiovascular diseases（Nature Reviews Cardiology'2019）
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TMI
- Learning Domain Shift in Simulated and Clinical Data: Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms（2019）
- Noninvasive Reconstruction of Transmural Transmembrane Potential With Simultaneous Estimation of Prior Model Erro（2019）
- Mapping Biological Current Densities With Ultrafast Acoustoelectric Imaging: Application to the Beating Rat Heart（2019）
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MIA
- A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs（2021）
- A rule-based method for predicting the electrical activation of the heart with cardiac resynchronization therapy from non-invasive clinical data（2019）
- Phase-aware echocardiogram stabilization using keyframes（2016）
- Analysis of nonstandardized stress echocardiography sequences using multiview dimensionality reduction（2019）
- A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations（2021）
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MICCAI