1. Analyze the Request: * Role: Professional news layout engineer. * Task: Convert plain text news article into a semantic HTML fragment. * Rules: 1. Only output the HTML fragment itself (no Markdown fences, no explanations). 2. Use `

` for paragraphs, `

`/`

` for sub-sections in long text. 3. Lists use `

• `, quotes use `

  `, code uses `

  `, important sentences use ``.
          4. Keep all original information, no additions/de removals.
          5. No ``, ``, `` tags.
      *   Input Text: A research abstract about ECG-SDI for sleep depth profiling.
  
  2.  Analyze the Input Text:
      "Conventional sleep staging provides a coarse-grained representation that obscures within-stage depth variation. We developed ECG-SDI, a single-lead electrocardiography (ECG) framework for continuous sleep-depth profiling and digital biomarker extraction. Using 12,392 overnight polysomnographic recordings, we trained and validated a two-stage deep learning model supervised by conventional sleep-stage labels. Correlations with ordered stages excluding rapid eye movement (REM) sleep were 0.828 in the pooled internal test set and 0.848 in the external Cleveland Family Study; corresponding REM classification AUROCs were 0.954 and 0.977. Overnight trajectories captured within-stage variation. Biomarker-based phenotyping identified a disturbed subtype associated with poorer subjective sleep quality and higher all-cause mortality in the Sleep Heart Health Study (adjusted hazard ratio, 1.20; 95% CI, 1.05-1.38). Among 507 participants with paired recordings before and after continuous positive airway pressure (CPAP), normalized sleep-depth area increased by 6.1% and shallow-sleep burden below 0.3 decreased by 16.0%. Exploratory analyses linked reductions in approximate entropy and depth variability to reduced oxygen desations and increased sleep efficiency, respectively (Speman rho = 0.24 and 0.12); both associations remained positive after covariate adjustment. Mean ECG-SDI within N2 increased by 0.017 (95% CI, 0.004-0.030). A wearable proof of concept illustrated the acquisition-to-report workflow. These findings support ECG-based sleep phenotyping and further evaluation of CPAP response monitoring at home."
  
  3.  Determine Structure & Semantics:
      *   The text is a single paragraph currently, but it contains several distinct logical sections (Method, Results/Validation, Clinical Application/Phenotyping, CPAP Response, Conclusion). To make it "semanticized" and use headings (`
  `/`
  `), I should break it into logical subsections.
      *   *Paragraph 1 (Introduction):* Conventional sleep staging provides a coarse-grained representation that obscures within-stage depth variation. We developed ECG-SDI, a single-lead electrocardiography (ECG) framework for continuous sleep-depth profiling and digital biomarker extraction.
      *   *Subsection 2 (Methodology):* Using 12,392 overnight polysomnographic recordings, we trained and validated a two-stage deep learning model supervised by conventional sleep-stage labels.
      *   *Subsection 3 (Validation):* Correlations with ordered stages excluding rapid eye movement (REM) sleep were 0.828 in the pooled internal test set and 0.848 in the external Cleveland Family Study; corresponding REM classification AUROCs were 0.954 and 0.977. Overnight trajectories captured within-stage variation.
      *   *Subsection 4 (Clinical Phenotyping):* Biomarker-based phenotyping identified a disturbed subtype associated with poorer subjective sleep quality and higher all-cause mortality in the Sleep Heart Health Study (adjusted hazard ratio, 1.20; 95% CI, 1.05-1.38).
      *   *Subsection 5 (CPAP Response):* Among 507 participants with paired recordings before and after continuous positive airway pressure (CPAP), normalized sleep-depth area increased by 6.1% and shallow-sleep burden below 0.3 decreased by 16.0%.
      *   *Subsection 6 (Exploratory Analyses):* Exploratory analyses linked reductions in approximate entropy and depth variability to reduced oxygen desaturation and increased sleep efficiency, respectively (Speman rho = 0.24 and 0.12); both associations remained positive after covariate adjustment. Mean ECG-SDI within N2 increased by 0.017 (95% CI, 0.004-0.030).
      *   *Subsection 7 (Conclusion):* A wearable proof of concept illustrated the acquisition-to-report workflow. These findings support ECG-based sleep phenotyping and further evaluation of CPAP response monitoring at home.
  
      Let's refine the headings to be natural news subheadings:
      *   
  Introduction
      *   
  Model Development and Validation
   (or 
  Model Development
  , 
  Validation Metrics
  ) -> Let's use 
  Model Development and Validation
   containing 
   and maybe  for key stats.
      *   
  Clinical Phenotyping
  
      *   
  CPAP Response Analysis
      *   
  Exploratory Analyses
      *   
  Conclusion
  
      Wait, the prompt says "长文用
  /
  划分小节". Let's structure it like an academic news article or summary.
      Title: (Wait, no `//`, but can I have `
  `? The prompt doesn't forbid `
  `, but usually news articles have a title. However, the input text doesn't have a title explicitly separated. I will just use `
  ` for the main sections and `
  ` for paragraphs.)
  
      Let's draft the structure:
      
  Conventional sleep staging provides a coarse-grained representation that obscures within-stage depth variation. We developed ECG-SDI, a single-lead electrocardiography (ECG) framework for continuous sleep-depth profiling and digital biomarker extraction.
  
      
  Methodology
      
  Using 12,392 overnight polysomnographic recordings, we trained and validated a two-stage deep learning model supervised by conventional sleep-stage labels.
  
      
  Validation Results
      
  Correlations with ordered stages excluding rapid eye movement (REM) sleep were 0.828 in the pooled internal test set and 0.848 in the external Cleveland Family Study; corresponding REM classification AUROCs were 0.954 and 0.977. Overnight trajectories captured within-stage variation.
  
      
  Clinical Phenotyping
      
  Biomarker-based phenotyping identified a disturbed subtype associated with poorer subjective sleep quality and higher all-cause mortality in the Sleep Heart Health Study (adjusted hazard ratio, 1.20; 95% CI, 1.05-1.38).
  
      
  CPAP Response Monitoring
      
  Among 507 participants with paired recordings before and after continuous positive airway pressure (CPAP), normalized sleep-depth area increased by 6.1% and shallow-sleep burden below 0.3 decreased by 16.0%.
  
      
  Exploratory Analysies
      
  Exploratory analyses linked reductions in approximate entropy and depth variability to reduced oxygen desaturation and increased sleep efficiency, respectively (Speman rho = 0.24 and 0.12); both associations remained positive after covariate adjustment. Mean ECG-SDI within N2 increased by 0.017 (95% CI, 0.004-0.030).
  
      
  Conclusion
      
  A wearable proof of concept illustrated the acquisition-to-report workflow. These findings support ECG-based sleep phenotyping and further evaluation of CPAP