所提出的17层1D CNN模型参数
用于从PPG信号检测心律失常的1D CNN模型参数，基于UMMC数据训练，可识别NSR、AF和PAC。
基本信息
资源简介
所提出的17层1D CNN模型参数
【数据集背景】
本数据集源站标题为《Parameters of the proposed 17-layer 1D CNN model.》。源站首次发布：2025-02-12。
【数据内容】
源站原始描述：Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachycardia) or too slow (bradycardia), irregular heartbeats (like atrial fibrillation-AF, ventricular fibrillation-VF), or the initiation of heartbeats in different areas from the norm (extrasystole). Arrhythmias can disrupt the balanced circulation, leading to serious complications like heart attacks, strokes, and sudden death. Medical devices like electrocardiography (ECG) and Holter monitors are com…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 1.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0314154.t001。
【主题与分类】
源站学科分类：Space Science、Medicine、Cell Biology、Physiology、Biological Sciences not elsewhere classified、Cancer。主题标签：provide earlier diagnoses、premature atrial contractions、massachusetts medical center、like atrial fibrillation、ppg signal data、div >< p、cardiac rhythm disorders、proposed model achieved、raw ppg signals、ppg signals using。
【适用方向】
源站学科分类标注为 Space Science、Medicine、Cell Biology、Physiology、Biological Sciences not elsewhere classified、Cancer。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Parameters_of_the_proposed_17-layer_1D_CNN_model_/28402018
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Bulut, Miray Gunay; Unal, Sencer; Hammad, Mohamed; Pławiak, Paweł (2025). Parameters of the proposed 17-layer 1D CNN model.. PLOS ONE. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Parameters of the proposed 17-layer 1D CNN model.
- DOI：10.1371/journal.pone.0314154.t001
- 发布日期：2025-02-12
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Parameters_of_the_proposed_17-layer_1D_CNN_model_/28402018
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/52310143"
文件清单
Table 1.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachycardia) or too slow (bradycardia), irregular heartbeats (like atrial fibrillation-AF, ventricular fibrillation-VF), or the initiation of heartbeats in different areas from the norm (extrasystole). Arrhythmias can disrupt the balanced circulation, leading to serious complications like heart attacks, strokes, and sudden death. Medical devices like electrocardiography (ECG) and Holter monitors are commonly used for diagnosing and monitoring cardiac rhythm disorders. However, in recent years, the development of wearable devices has played a significant role in the detection and diagnosis of rhythm disorders through the use of photoplethysmography (PPG) signals. Wearable devices enable patients to continuously monitor their health status and allow doctors to provide earlier diagnoses and interventions. In this study, a 1D-CNN model is proposed to detect arrhythmias using PPG signals. A dataset prepared by the University of Massachusetts Medical Center (UMMC) containing both ECG and PPG signal data was utilized. In this dataset, ECG signals are filtered with a bandpass filter and raw PPG signals are divided into 30-second segments. Accuracy values were obtained by classifying ECG and PPG signals using a 1D CNN model. ECG signals were used as a reference. The proposed model achieved a 95.17% accuracy rate in detecting normal sinus rhythm (NSR), atrial fibrillation (AF), and premature atrial contractions (PAC) from PPG signals. Datasets are available for download on https://www.synapse.org/pulsewatch. The codes used in this study are available on the https://github.com/miraygunay/PPG-Code.git website.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0314154.t001（source: https://plos.figshare.com/articles/dataset/Parameters_of_the_proposed_17-layer_1D_CNN_model_/28402018）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
