机器学习检测房颤使用可穿戴技术

数据集获取（Figshare）

• 源站标题：Machine learning detection of Atrial Fibrillation using wearable technology
• DOI：10.1371/journal.pone.0227401
• 发布日期：2020-01-24
• 文件数：3 个
• 文件总体积：394.54 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Machine_learning_detection_of_Atrial_Fibrillation_using_wearable_technology/11713053

命令行下载

curl -L -o "pone.0227401.s001.xlsx" "https://ndownloader.figshare.com/files/21309231"
curl -L -o "pone.0227401.s002.7z" "https://ndownloader.figshare.com/files/21309234"
curl -L -o "pone.0227401.s003.docx" "https://ndownloader.figshare.com/files/21309237"

文件清单

• pone.0227401.s001.xlsx（19.49 KB）
• pone.0227401.s002.7z（35.63 KB）
• pone.0227401.s003.docx（339.42 KB）

文件总体积 394.54 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Background
Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatment using warfarin or direct oral anticoagulants is effective in reducing the risk of AF-related stroke by approximately two-thirds and can provide a 10% reduction in overall mortality. There has been increased interest in detecting AF due to its increased incidence and the possibility to prevent AF-related strokes. Inexpensive consumer devices which measure the ECG may have the potential to accurately detect AF but do not generally incorporate diagnostic algorithms. Machine learning algorithms have the potential to improve patient outcomes particularly where diagnoses are made from large volumes or complex patterns of data such as in AF.
Methods
We designed a novel AF detection algorithm using a de-correlated Lorenz plot of 60 consecutive RR intervals. In order to reduce the volume of data, the resulting images were compressed using a wavelet transformation (JPEG200 algorithm) and the compressed images were used as input data to a Support Vector Machine (SVM) classifier. We used the Massachusetts Institute of Technology (MIT)—Beth Israel Hospital (BIH) Atrial Fibrillation database and the MIT-BIH Arrhythmia database as training data and verified the algorithm performance using RR intervals collected using an inexpensive consumer heart rate monitor device (Polar-H7) in a case-control study.
Results
The SVM algorithm yielded excellent discrimination in the training data with a sensitivity of 99.2% and a specificity of 99.5% for AF. In the validation data, the SVM algorithm correctly identified AF in 79/79 cases; sensitivity 100% (95% CI 95.4%-100%) and non-AF in 328/336 cases; specificity 97.6% (95% CI 95.4%-99.0%).
Conclusions
An inexpensive wearable heart rate monitor and machine learning algorithm can be used to detect AF with very high accuracy and has the capability to transmit ECG data which could be used to confirm AF. It could potentially be used for intermittent screening or continuously for prolonged periods to detect paroxysmal AF. Further work could lead to cost-effective and accurate estimation of AF burd

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0227401（source: https://plos.figshare.com/articles/dataset/Machine_learning_detection_of_Atrial_Fibrillation_using_wearable_technology/11713053）。