Exploration and Analysis of Traditional Chinese Medicine Syndrome Differentiation of Insomnia Based on Millimeter-Wave Radar

To address the lack of continuous and objective nocturnal sign data in TCM insomnia pattern differentiation, this study explores the objective characterization method and digital-intelligent identification pathway for insomnia syndromes based on millimeter-wave radar. This review systematically searched CNKI, Wanfang, PubMed, and Web of Science from January 2008 to August 2026, covering literature on TCM syndrome differentiation and treatment of insomnia, heart rate variability and autonomic function, and non-contact sleep/respiration monitoring using millimeter-wave radar. Taking the TCM pathogenesis of the five major insomnia syndromes as the starting point and incorporating modern sleep physiology, the internal "syndrome-autonomic/sign" association mechanism is deduced. The non-contact motion signals captured by radar are transformed into primary vital sign sequences such as respiration, heart rate, and body movement. Based on these primary signals, a set of 15 quantitative indicators covering four dimensions-sleep architecture, autonomic nervous function, respiratory pattern, and body movement behavior-is derived. Among them, primary indicators are obtained by direct extraction or simple statistics, whereas sleep architecture, microarousal, and apnea-hypopnea index (AHI) indicators are estimated by algorithmic models and require validation against polysomnography (PSG). A pathogenesis-physiology mapping model linking the five insomnia syndromes with radar signal features is established, forming a TCM sleep digital representation system with 15 standardized parameters, which provides a theoretical computational basis for decoupling the microscopic differences among syndromes from non-contact signals. Millimeter-wave radar can serve as an effective extension of the traditional four diagnostic methods, offering non-intrusive and continuous nocturnal data supplementation for insomnia syndrome differentiation. The indicator system and theoretical framework constructed in this study lay a methodological foundation for subsequent clinical data validation and the development of home-based, digital-intelligent TCM sleep assessment devices.​.