Automatic Detection and classification of Cardiac abnormalities and Arrhythmias from a limited number of ECG signals is of considerable importance in critical care or operating room patient monitoring

NIAID Data Ecosystem60

TF-C Pretrain ECG

心电图分类

心律失常预测

- Paper: Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency - Paper link: - Github repo: https://github.com/mims-harvard/TFC-pretraining - Project website: <s

Figshare2022-05-31 更新30

AF_Classification_from_a_Short_etc

心电图分类

心律失常检测

2017 年 PhysioNet/CinC 挑战赛旨在鼓励开发算法来分类，从单个短 ECG 导联记录（长度在 30 秒到 60 秒之间），记录是否显示正常窦性心律、心房颤动 (AF)、替代节奏，或者太吵而无法分类。有多种类型的心律失常可按以下分类：起源：房性心律失常、交界性心律失常或室性心律失常。频率：心动过速（成人 > 100 次每分钟 (bpm)）或心动过缓（成人 < 60 bpm）。

OpenXLab90

ECG Dataset for Heart Condition Classification

心电图分类

心脏疾病诊断

This ECG dataset comprises three distinct classes: normal, abnormal, and disease-specific cardiac signals. Collected from both healthy individuals and patients with heart conditions, the dataset provi

NIAID Data Ecosystem50

Electrocardiograms for Cardiac Arrest Rhythm Identification

资源简介：

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