IEEE J Biomed Health Inform. 2024 Oct;28(10):5718-5728. doi: 10.1109/JBHI.2024.3403109. Epub 2024 Oct 3.
Intraoperative hypotension can lead to postoperative organ dysfunction. Previous studies primarily used invasive arterial pressure as the key biosignal for the detection of hypotension. However, these studies had limitations in incorporating different biosignal modalities and utilizing the periodic nature of biosignals. To address these limitations, we utilized frequency-domain information, which provides key insights that time-domain analysis cannot provide, as revealed by recent advances in deep learning. With the frequency-domain information, we propose a deep-learning approach that integrates multiple biosignal modalities.
We used the discrete Fourier transform technique, to extract frequency information from biosignal data, which we then combined with the original time-domain data as input for our deep learning model. To improve the interpretability of our results, we incorporated recent interpretable modules for deep-learning models into our analysis.
We constructed 75 994 segments from the data of 3226 patients to predict hypotension during surgery. Our proposed frequency-domain deep-learning model outperformed conventional approaches that rely solely on time-domain information. Notably, our model achieved a greater increase in AUROC performance than the time-domain deep learning models when trained on non-invasive biosignal data only (AUROC 0.898 [95% CI: 0.885-0.91] vs. 0.853 [95% CI: 0.839-0.867]). Further analysis revealed that the 1.5-3.0 Hz frequency band played an important role in predicting hypotension events.
Utilizing the frequency domain not only demonstrated high performance on invasive data but also showed significant performance improvement when applied to non-invasive data alone. Our proposed framework offers clinicians a novel perspective for predicting intraoperative hypotension.
术中低血压可导致术后器官功能障碍。先前的研究主要使用有创动脉压作为检测低血压的关键生物信号。然而，这些研究在整合不同的生物信号模态和利用生物信号的周期性方面存在局限性。为了解决这些局限性，我们利用了频域信息，这为深度学习的最新进展提供了时间域分析无法提供的关键见解。利用频域信息，我们提出了一种整合多种生物信号模态的深度学习方法。
我们使用离散傅里叶变换技术从生物信号数据中提取频率信息，然后将其与原始时域数据结合作为我们深度学习模型的输入。为了提高结果的可解释性，我们在分析中纳入了深度学习模型的最新可解释模块。
我们从 3226 名患者的数据中构建了 75994 个片段来预测手术期间的低血压。我们提出的频域深度学习模型优于仅依赖时域信息的传统方法。值得注意的是，当仅使用非侵入性生物信号数据训练时，我们的模型在 AUROC 性能上的提高幅度大于时域深度学习模型（AUROC 0.898 [95%CI：0.885-0.91] 与 0.853 [95%CI：0.839-0.867]）。进一步分析表明，1.5-3.0 Hz 频段在预测低血压事件中起着重要作用。
利用频域不仅在有创数据上表现出高性能，而且在单独应用于非侵入性数据时也显示出显著的性能提升。我们提出的框架为临床医生预测术中低血压提供了新的视角。
