可穿戴心律转复除颤器治疗优化计算模型数据表1

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy.PDF
• DOI：10.3389/fphys.2021.787180.s001
• 发布日期：2021-12-10
• 文件数：1 个
• 文件总体积：220.37 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Computational_Model_for_Therapy_Optimization_of_Wearable_Cardioverter_Defibrillator_Shockable_Rhythm_Detection_and_Optimal_Electrotherapy_PDF/17157374

命令行下载

curl -L -o "Data_Sheet_1_Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherap.PDF" "https://ndownloader.figshare.com/files/31721606"

文件清单

• Data_Sheet_1_Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherap.PDF（220.37 KB）

文件总体积 220.37 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

Wearable cardioverter defibrillator (WCD) is a life saving, wearable, noninvasive therapeutic device that prevents fatal ventricular arrhythmic propagation that leads to sudden cardiac death (SCD). WCD are frequently prescribed to patients deemed to be at high arrhythmic risk but the underlying pathology is potentially reversible or to those who are awaiting an implantable cardioverter-defibrillator. WCD is programmed to detect appropriate arrhythmic events and generate high energy shock capable of depolarizing the myocardium and thus re-initiating the sinus rhythm. WCD guidelines dictate very high reliability and accuracy to deliver timely and optimal therapy. Computational model-based process validation can verify device performance and benchmark the device setting to suit personalized requirements. In this article, we present a computational pipeline for WCD validation, both in terms of shock classification and shock optimization. For classification, we propose a convolutional neural network-“Long Short Term Memory network (LSTM) full form” (Convolutional neural network- Long short term memory network (CNN-LSTM)) based deep neural architecture for classifying shockable rhythms like Ventricular Fibrillation (VF), Ventricular Tachycardia (VT) vs. other kinds of non-shockable rhythms. The proposed architecture has been evaluated on two open access ECG databases and the classification accuracy achieved is in adherence to American Heart Association standards for WCD. The computational model developed to study optimal electrotherapy response is an in-silico cardiac model integrating cardiac hemodynamics functionality and a 3D volume conductor model encompassing biophysical simulation to compute the effect of shock voltage on myocardial potential distribution. Defibrillation efficacy is simulated for different shocking electrode configurations to assess the best defibrillator outcome with minimal myocardial damage. While the biophysical simulation provides the field distribution through Finite Element Modeling during defibrillation, the hemodynamic module captures the changes in left ventricle functionality during an arrhythmic event. The developed computational mod

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fphys.2021.787180.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Computational_Model_for_Therapy_Optimization_of_Wearable_Cardioverter_Defibrillator_Shockable_Rhythm_Detection_and_Optimal_Electrotherapy_PDF/17157374）。