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Abstract <p>The diagnostic armamentarium for sleep-disordered breathing (SDB) has undergone substantial expansion in recent years, alongside the heightened awareness of SDB, its systemic impact on health, and the proliferation of home sleep apnea testing (HSAT). Many contemporary diagnostic modalities incorporate physiological measurement techniques that draw upon concepts primarily investigated in research paradigms, but may not align with existing HSAT classification frameworks, particularly regarding respiratory signal acquisition and analysis. This narrative review synthesizes current respiratory analysis techniques employed in SDB diagnosis, categorizing them broadly into three principal measurement domains: airflow dynamics, respiratory effort mechanics, and sympathetic activation patterns. Emphasis is placed on the historical background, underlying physiological mechanism, and clinical utility, while evaluating the methodological strengths and inherent limitations of each measurement approach. </p> Brief summary <p>While prior literature reviews have extensively documented conventional respiratory analysis methodologies in sleep apnea diagnostics, the rapidly evolving landscape of cutting-edge diagnostic technologies and their physiological signals remain insufficiently addressed in the current literature. This respiratory technology-centered narrative review bridges this crucial knowledge gap through a rigorous and comprehensive examination of both established and emerging respiratory assessment modalities. Furthermore, we posit that the existing sleep testing classification frameworks no longer adequately reflect the breadth and sophistication of contemporary diagnostic technologies, and that their revision represents a pressing imperative for the sleep medicine community. We advocate a reconceptualization of sleep testing taxonomies to ensure clinical relevance and diagnostic utility amid accelerating technological evolution.</p>

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From classic to cutting-edge: technological approaches to respiratory physiological signals in assessing sleep-disordered breathing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • Ambrose A. Chiang,
  • Joyce Lee-Iannotti,
  • Brennan Torstrick,
  • Richard B. Berry,
  • Nancy A. Collop

摘要

Abstract

The diagnostic armamentarium for sleep-disordered breathing (SDB) has undergone substantial expansion in recent years, alongside the heightened awareness of SDB, its systemic impact on health, and the proliferation of home sleep apnea testing (HSAT). Many contemporary diagnostic modalities incorporate physiological measurement techniques that draw upon concepts primarily investigated in research paradigms, but may not align with existing HSAT classification frameworks, particularly regarding respiratory signal acquisition and analysis. This narrative review synthesizes current respiratory analysis techniques employed in SDB diagnosis, categorizing them broadly into three principal measurement domains: airflow dynamics, respiratory effort mechanics, and sympathetic activation patterns. Emphasis is placed on the historical background, underlying physiological mechanism, and clinical utility, while evaluating the methodological strengths and inherent limitations of each measurement approach.

Brief summary

While prior literature reviews have extensively documented conventional respiratory analysis methodologies in sleep apnea diagnostics, the rapidly evolving landscape of cutting-edge diagnostic technologies and their physiological signals remain insufficiently addressed in the current literature. This respiratory technology-centered narrative review bridges this crucial knowledge gap through a rigorous and comprehensive examination of both established and emerging respiratory assessment modalities. Furthermore, we posit that the existing sleep testing classification frameworks no longer adequately reflect the breadth and sophistication of contemporary diagnostic technologies, and that their revision represents a pressing imperative for the sleep medicine community. We advocate a reconceptualization of sleep testing taxonomies to ensure clinical relevance and diagnostic utility amid accelerating technological evolution.