学习将心电图信号压缩到快速响应代码中
Apoorva Srivastava1, Dipayan Dewan2, Amit Patra2
1Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India. apoorva.s.2311@gmail.com.
Scientific reports
|December 1, 2025
概括
一种新的基于学习的压缩方法将心电图 (ECG) 数据嵌入到QR码中,以便安全共享. 这种方法增强了连接的卫生系统,改善了在资源较少的环境中检测心血管疾病 (CVD).
科学领域:
- 生物医学工程 生物医学工程
- 数字健康数字健康
- 医疗信息学 医疗信息学
背景情况:
- 电心电图 (ECG) 对于诊断心血管疾病 (CVD) 是至关重要的.
- 在低收入和中等收入国家 (LMICs) 分享心电图数据是具有挑战性的,因为有限的连接健康 (CH) 基础设施和与纸质记录相关的隐私风险.
- 快速响应 (QR) 代码为安全和高效的数据传输提供了潜在的解决方案.
研究的目的:
- 提出和验证一种基于学习的ECG数据压缩方法,适合QR码嵌入.
- 确保在心电图数据传输期间保护临床关键信息和患者隐私.
- 允许ECG数据集成到连接的卫生系统中,以改善心血管疾病监测和早期检测.
主要方法:
- 开发了一种基于学习的压缩技术,以保护来自心电图信号的关键临床信息.
- 压缩的心电图数据使用Brotli算法对QR码嵌入进行了无损编码.
- 该方法使用公共数据集验证了健康个体和26种心血管疾病病理的患者的ECG记录.
主要成果:
- 拟议的方法实现了82.37.7的压缩系数 (CF).
- 对于II心电图信号,PRD为2.70%,SSIM为0.94.4%.
- 对于-I心电图信号,PRD为2.80%,SSIM为0.94,表明数据的高保真性.
- 该方法在压缩和数据保存方面超过了现有的最先进的方法.
结论:
- 开发的基于学习的压缩和QR码嵌入方法为ECG数据传输提供了安全,高效和可扩展的解决方案.
- 这种方法有助于将心电图数据集成到连接的卫生系统中,特别是在资源有限的环境中.
- 该方法支持持续监测和早期发现心血管疾病,增强患者护理.
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