利用中心静脉压力波形预测每搏量变化:一种深度学习方法。

IF 2.3 4区 医学 Q3 BIOPHYSICS
Insun Park, Jae Hyon Park, Bon-Wook Koo, Jin-Hee Kim, Young-Tae Jeon, Hyo-Seok Na, Ah-Young Oh
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引用次数: 0

摘要

目的: 本研究评估了一种深度学习方法的预测性能,该方法可从中心静脉压(CVP)波形预测卒中容量变化(SVV):方法:利用从开源注册机构 VitalDB 数据库获取的 CVP 波形,对长短期记忆和前馈神经网络进行排序,以预测 SVV。长时短时记忆的输入包括在整个麻醉过程中以 2 秒间隔采样的 10 秒 CVP 波形。前馈网络的输入是长时短时记忆的输出和人口统计学数据,如年龄、性别、体重和身高。前馈网络的最终输出是 SVV。深度学习模型预测的 SVV 值与使用商业化模型 EV1000 通过动脉脉搏波形分析得出的 SVV 值进行了比较。共有 224 个病例,包括 1717978 个 CVP 波形和 EV1000/SVV 数据,用于构建和测试深度学习模型。深度学习模型估计的 SVV 与 EV1000 测量的 SVV 之间的一致性相关系数为 0.993(95% 置信区间 [CI],0.992-0.993)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting stroke volume variation using central venous pressure waveform: a deep learning approach.

Objective. This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.Approach. Long short-term memory (LSTM) and the feed-forward neural network were sequenced to predict SVV using CVP waveforms obtained from the VitalDB database, an open-source registry. The input for the LSTM consisted of 10 s CVP waveforms sampled at 2 s intervals throughout the anesthesia duration. Inputs of the feed-forward network were the outputs of LSTM and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the SVV. The performance of SVV predicted by the deep learning model was compared to SVV estimated derived from arterial pulse waveform analysis using a commercialized model, EV1000.Main results. The model hyperparameters consisted of 12 memory cells in the LSTM layer and 32 nodes in the hidden layer of the feed-forward network. A total of 224 cases comprising 1717 978 CVP waveforms and EV1000/SVV data were used to construct and test the deep learning models. The concordance correlation coefficient between estimated SVV from the deep learning model were 0.993 (95% confidence interval, 0.992-0.993) for SVV measured by EV1000.Significance. Using a deep learning approach, CVP waveforms can accurately approximate SVV values close to those estimated using commercial arterial pulse waveform analysis.

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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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