单导联心电图机器学习识别房颤诱发心力衰竭患者数据集

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure.docx
• DOI：10.3389/fcvm.2022.812719.s001
• 发布日期：2022-02-28
• 文件数：1 个
• 文件总体积：956.27 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_Learning_Using_a_Single-Lead_ECG_to_Identify_Patients_With_Atrial_Fibrillation-Induced_Heart_Failure_docx/19247307

命令行下载

curl -L -o "Data_Sheet_1_Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure.docx" "https://ndownloader.figshare.com/files/34201473"

文件清单

• Data_Sheet_1_Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure.docx（956.27 KB）

文件总体积 956.27 KB（源站 API 实测）。

数据说明

AimsAtrial fibrillation (AF) and heart failure often co-exist. Early identification of AF patients at risk for AF-induced heart failure (AF-HF) is desirable to reduce both morbidity and mortality as well as health care costs. We aimed to leverage the characteristics of beat-to-beat-patterns in AF to prospectively discriminate AF patients with and without AF-HF.
MethodsA dataset of 10,234 5-min length RR-interval time series derived from 26 AF-HF patients and 26 control patients was extracted from single-lead Holter-ECGs. A total of 14 features were extracted, and the most informative features were selected. Then, a decision tree classifier with 5-fold cross-validation was trained, validated, and tested on the dataset randomly split. The derived algorithm was then tested on 2,261 5-min segments from six AF-HF and six control patients and validated for various time segments.
ResultsThe algorithm based on the spectral entropy of the RR-intervals, the mean value of the relative RR-interval, and the root mean square of successive differences of the relative RR-interval yielded an accuracy of 73.5%, specificity of 91.4%, sensitivity of 64.7%, and PPV of 87.0% to correctly stratify segments to AF-HF. Considering the majority vote of the segments of each patient, 10/12 patients (83.33%) were correctly classified.
ConclusionBeat-to-beat-analysis using a machine learning classifier identifies patients with AF-induced heart failure with clinically relevant diagnostic properties. Application of this algorithm in routine care may improve early identification of patients at risk for AF-induced cardiomyopathy and improve the yield of targeted clinical follow-up.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2022.812719.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_Learning_Using_a_Single-Lead_ECG_to_Identify_Patients_With_Atrial_Fibrillation-Induced_Heart_Failure_docx/19247307）。