Scholarlink

<p>Accurate respiratory rate (RR) measurement is essential for early detection of physiological deterioration, yet conventional approaches remain limited by poor robustness and restricted suitability for continuous or remote monitoring. ECG-derived respiratory rate (EDRR) offers a non-invasive alternative by exploiting respiration-induced modulations in cardiac electrical activity, including morphological changes, heart rate variability, and their combined effects. This review provides a structured synthesis of EDRR methods, spanning physiological mechanisms, signal acquisition and preprocessing considerations, and algorithmic approaches including morphology-based, autonomic (heart rate variability/spectral), and fusion strategies. We further evaluate datasets, validation practices, and performance trade-offs across controlled and real-world conditions. Key challenges include susceptibility to motion artifacts, inter-subject variability, inconsistent evaluation protocols, and limited generalizability in ambulatory settings. Addressing these limitations is critical for translating EDRR techniques into robust, scalable solutions for wearable and telehealth-based respiratory monitoring.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Electrocardiogram-derived respiratory rate: State-of-the-art and implications for remote cardiopulmonary monitoring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

查看全文
  • Carmen Martínez Antón,
  • Zakaria El Ghebouli,
  • Vladimír Sobota,
  • Amaël Mombereau,
  • Laura Bear,
  • Jesse D. Roberts Jr.,
  • Kanchan Kulkarni

摘要

Accurate respiratory rate (RR) measurement is essential for early detection of physiological deterioration, yet conventional approaches remain limited by poor robustness and restricted suitability for continuous or remote monitoring. ECG-derived respiratory rate (EDRR) offers a non-invasive alternative by exploiting respiration-induced modulations in cardiac electrical activity, including morphological changes, heart rate variability, and their combined effects. This review provides a structured synthesis of EDRR methods, spanning physiological mechanisms, signal acquisition and preprocessing considerations, and algorithmic approaches including morphology-based, autonomic (heart rate variability/spectral), and fusion strategies. We further evaluate datasets, validation practices, and performance trade-offs across controlled and real-world conditions. Key challenges include susceptibility to motion artifacts, inter-subject variability, inconsistent evaluation protocols, and limited generalizability in ambulatory settings. Addressing these limitations is critical for translating EDRR techniques into robust, scalable solutions for wearable and telehealth-based respiratory monitoring.