Electrocardiography (ECG)
31 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
Use these libraries to find Electrocardiography (ECG) models and implementations
MIMIC PERform Testing Dataset
MIMIC-IV-ECG
Subtasks
Latest papers with no code
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%.
ECGNet: A generative adversarial network (GAN) approach to the synthesis of 12-lead ECG signals from single lead inputs
The GAN models have achieved remarkable results in reproducing ECG signals but are only designed for multiple lead inputs and the features the GAN model preserves have not been identified-limiting the generated signals use in cardiovascular disease (CVD)-predictive models.
Comparison of HRV Indices of ECG and BCG Signals
Electrocardiography (ECG) plays a significant role in diagnosing heart-related issues, it provides, accurate, fast, and dependable insights into crucial parameters like QRS complex duration, the R-R interval, and the occurrence, amplitude, and duration of P, R, and T waves.
Automated Identication of Atrial Fibrillation from Single-lead ECGs Using Multi-branching ResNet
Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm.
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study
However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models.
Subject-based Non-contrastive Self-Supervised Learning for ECG Signal Processing
Supervised learning methods have successfully been used to identify specific aspects in the signal, like detection of rhythm disorders (arrhythmias).
Predicting Pulmonary Hypertension by Electrocardiograms Using Machine Learning
Pulmonary hypertension (PH) is a condition of high blood pressure that affects the arteries in the lungs and the right side of the heart (Mayo Clinic, 2017).
Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata
This work discusses the use of contrastive learning and deep learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals.
OpenDriver: an open-road driver state detection dataset
Therefore, in this paper, a large-scale multimodal driving dataset for driver impairment detection and biometric data recognition is designed and described.
Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage
As sleep disorders are becoming more prevalent there is an urgent need to classify sleep stages in a less disturbing way. In particular, sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), or electrocardiography (ECG) has gained substantial interest.
