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Refinamiento de las anotaciones de la respiración del sueño en múltiples conjuntos de datos públicos de estudios del

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Resumen

Una nueva tubería estandarizada mejora la anotación de la polisomnografía para la investigación de la apnea del sueño. Esto mejora la precisión en el cálculo del índice de apnea-hipopnea y la clasificación de la gravedad de la apnea obstructiva del sueño.

Palabras clave:
anotación de datoscalidad de datosaprendizaje profundopolisomnografíamejora de la calidadrespiración del sueño

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Área de la Ciencia:

  • Medicina del Sueño
  • Ciencia de Datos Biomédicos
  • Inteligencia Artificial en la Atención Médica

Sus antecedentes:

  • Las anotaciones existentes de polisomnografía en estudios importantes del sueño (SHHS, MrOS, MESA) utilizaron criterios simplificados para apneas/hipopneas, desviándose de las pautas de la Academia Estadounidense de Medicina del Sueño (AASM).
  • Esta inconsistencia conduce a índices de apnea-hipopnea (IAH) inexactos y conclusiones de investigación potencialmente erróneas.

Objetivo del estudio:

  • Desarrollar y validar un pipeline de anotación estandarizado para datos de polisomnografía que integre etapas del sueño, desaturación de oxígeno y eventos de despertar según los criterios de la AASM.
  • Reevaluar el IAH y la gravedad de la apnea obstructiva del sueño (AOS) en cohortes grandes utilizando anotaciones refinadas.
  • Evaluar el impacto de las anotaciones mejoradas en el rendimiento del modelo de aprendizaje profundo para la clasificación de la AOS.

Principales métodos:

  • Se desarrolló un pipeline de anotación estandarizado que incorpora las pautas de la AASM para la etapa del sueño, la desaturación de oxígeno y los eventos de despertar.
  • Se analizaron retrospectivamente datos de polisomnografía de las cohortes SHHS1, SHHS2, MrOS1, MrOS2, MESA y KISS.
  • Se compararon los valores originales de IAH con los IAH recalculados utilizando el pipeline refinado y se entrenaron modelos de aprendizaje profundo con las anotaciones originales y refinadas.

Principales resultados:

  • Las anotaciones originales en SHHS, MrOS y MESA sobreestimaron significativamente los IAH (MAE: 10,3–23,6 eventos/h).
  • Las anotaciones refinadas redujeron drásticamente el MAE a 0,56–1,29 eventos/h, lo que demuestra una mayor precisión.
  • El rendimiento del modelo de aprendizaje profundo para la clasificación de la gravedad de la AOS mejoró, con una puntuación F1 que aumentó del 0,5 al 0,69 después del refinamiento de las anotaciones.

Conclusiones:

  • El pipeline de anotación estandarizado mejora la consistencia y precisión entre cohortes en el análisis de polisomnografía.
  • Este enfoque permite una utilización más confiable de los conjuntos de datos de estudios del sueño existentes para la investigación clínica y las aplicaciones impulsadas por IA.
  • La adhesión a las pautas de la AASM a través de anotaciones estandarizadas es crucial para la evaluación e investigación precisas de la apnea del sueño.