Neonatal Intensive Care Unit, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
University of Cambridge, St. Edmund's College, Cambridge, UK.
Pediatr Res. 2021 May;89(6):1432-1441. doi: 10.1038/s41390-020-01301-9. Epub 2020 Dec 7.
Modern neonatal ventilators allow the downloading of their data with a high sampling rate. We wanted to develop an algorithm that automatically recognises and characterises ventilator inflations from ventilator pressure and flow data.
We downloaded airway pressure and flow data with 100 Hz sampling rate from Dräger Babylog VN500 ventilators ventilating critically ill infants. We developed an open source Python package, Ventiliser, that includes a rule-based algorithm to automatically discretise ventilator data into a sequence of flow and pressure states and to recognise ventilator inflations and an information gain approach to identify inflation phases (inspiration, expiration) and sub-phases (pressure rise, pressure plateau, inspiratory hold etc.).
Ventiliser runs on a personal computer and analyses 24 h of ventilation in 2 min. With longer recordings, the processing time increases linearly. It generates a table reporting indices of each breath and its sub-phases. Ventiliser also allows visualisation of individual inflations as waveforms or loops. Ventiliser identified >97% of ventilator inflations and their sub-phases in an out-of-sample validation of manually annotated data. We also present detailed quantitative analysis and comparison of two 1-hour-long ventilation periods.
Ventiliser can analyse ventilation patterns and ventilator-patient interactions over long periods of mechanical ventilation.
We have developed a computational method to recognize and analyse ventilator inflations from raw data downloaded from ventilators of preterm and critically ill infants. There have been no previous reports on the computational analysis of neonatal ventilator data. We have made our program, Ventiliser, freely available. Clinicians and researchers can use Ventiliser to analyse ventilator inflations, waveforms and loops over long periods. Ventiliser can also be used to study ventilator-patient interactions.
现代新生儿呼吸机可以以高采样率下载其数据。我们希望开发一种算法，能够自动识别和描述呼吸机压力和流量数据中的呼吸机充气。
我们从 Dräger Babylog VN500 呼吸机下载了 100Hz 采样率的气道压力和流量数据，这些数据用于为危重新生儿提供通气。我们开发了一个开源的 Python 包 Ventiliser，它包括一个基于规则的算法，可以自动将呼吸机数据离散化为一系列流量和压力状态，并识别呼吸机充气，以及一种信息增益方法来识别充气阶段（吸气、呼气）和子阶段（压力上升、压力平台、吸气保持等）。
Ventiliser 在个人计算机上运行，可在 2 分钟内分析 24 小时的通气。随着记录时间的延长，处理时间呈线性增加。它生成一个报告每个呼吸及其子阶段的指标的表格。Ventiliser 还允许以波形或循环的形式可视化单个充气。Ventiliser 在对手动标记数据的样本外验证中，能够识别超过 97%的呼吸机充气及其子阶段。我们还提供了两个 1 小时通气期的详细定量分析和比较。
Ventiliser 可以分析长时间机械通气期间的通气模式和呼吸机-患者相互作用。
我们已经开发了一种计算方法，能够从从早产儿和危重新生儿呼吸机下载的原始数据中识别和分析呼吸机充气。以前没有关于新生儿呼吸机数据的计算分析的报道。我们已经免费提供了我们的程序 Ventiliser。临床医生和研究人员可以使用 Ventiliser 长时间分析呼吸机充气、波形和循环。Ventiliser 还可用于研究呼吸机-患者相互作用。
