Carotid Doppler Audio Dataset for Stroke Risk Prediction: 149 Recordings from 75 Patients

Doppler audio analysis framework extracting 24 hemodynamic features with Smart Score (1-5) and Risk Score (0-1). Open dataset: 149 recordings from 75 patients with clinical metadata for stroke screening. Doppler_v1/ ├── README.md # Project overview, setup, usage, citation ├── LICENSE # MIT License ├── .gitignore # Excludes large dataset files ├── requirements.txt # Python dependencies ├── app.py # Main Flask web application ├── app.ipynb # Jupyter Notebook (algorithm development) ├── templates/ │ └── index.html # Web interface (Arabic/English) ├── PROCESSED/ │ ├── audio/ # Dataset: 149 WAV recordings (external link) │ ├── spectrum_images/ # Diagnostic PNG figures │ └── Result_Export/ # CSV/Excel reports with 24 features ├── dataset/ │ └── metadata.csv # Clinical annotations per recording └── docs/ └── user_manual.pdf # Platform usage guide本多普勒音频分析框架可提取24项血流动力学特征，并生成智能评分（Smart Score，取值范围1-5分）与风险评分（Risk Score，取值范围0-1分）。本数据集为开放数据集，包含75名卒中筛查患者的149条录音，附带对应临床元数据。 项目目录结构如下： Doppler_v1/ ├── README.md # 项目概述、环境搭建、使用说明与引用规范 ├── LICENSE # MIT许可证 ├── .gitignore # 用于排除大型数据集文件的配置文件 ├── requirements.txt # Python依赖项清单 ├── app.py # Flask主Web应用程序 ├── app.ipynb # 用于算法开发的Jupyter Notebook ├── templates/ │ └── index.html # 支持阿拉伯语、英语的Web交互界面 ├── PROCESSED/ │ ├── audio/ # 数据集：149条WAV格式录音（提供外部下载链接） │ ├── spectrum_images/ # 诊断用PNG格式频谱图像 │ └── Result_Export/ # 包含24项特征的CSV/Excel格式报告文件 ├── dataset/ │ └── metadata.csv # 每条录音对应的临床标注元数据 └── docs/ └── user_manual.pdf # 平台使用指南文档