电心电图信号压缩使用自适应调整Q波段转换和修改的死区量化器
Hardev Singh Pal1, A Kumar1, Amit Vishwakarma1
1Discipline of Electronics and Communication Engineering, PDPM Indian Institute ofInformation Technology, Design and Manufacturing Jabalpur, Jabalpur 482005, India.
ISA transactions
|July 31, 2023
概括
这项研究介绍了一种高效的心电图 (ECG) 压缩算法,使用自适应可调Q波段转换和Sparse灰狼优化. 该方法显著减少了远程医疗的数据大小,同时保持了信号完整性.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 数据压缩数据压缩
背景情况:
- 电心电图 (ECG) 信号对于诊断心脏病至关重要.
- 大量的心电图数据在远程医疗中带来了存储和带宽的挑战.
- 需要高效的压缩算法来有效管理心电图数据.
研究的目的:
- 提出一种新的,高效的心电图信号压缩算法.
- 为了优化压缩参数使用新的元启发算法.
- 评估算法的性能与现有方法相比.
主要方法:
- 适应调整Q波形变换 (TQWT) 用于信号分解.
- 修改的死区量化器 (DZQ) 用于值和量化.
- 稀疏灰狼优化 (Sparse-GWO) 用于参数优化.
- 运行长度编码 (RLE) 用于高效的数据编码.
主要成果:
- 拟议的算法实现了20.56.6的压缩比 (CR).
- 保持高信号质量,百分比平方根平均差异 (PRD1) 为3.21%,信号与噪声比 (SNR) 为30.62dB.
- 与原始GWO和PSO变体相比,Sparse-GWO显示了较短的计算时间.
- 质量评分 (QS1) 的平均值为7.79,表明对心电图形态的影响最小.
结论:
- 新的ECG压缩算法提供了高压缩比,信号扭曲最小.
- Sparse-GWO提供了一种优化压缩参数的高效方法.
- 开发的算法适用于需要高效处理心电图数据的远程医疗应用.
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