Autors: Petrova R., Ahmed-Shieva, S. A., Shakev, N. G., Minev I.
Title: Deep Learning Estimation of Mechanical Power in Pressure-Controlled Ventilation Using a 1D CNN–Bidirectional LSTM Model †
Keywords: 1D convolutional neural network, Bidirectional Long Short-Term Memory, deep learning, feature masking, mechanical power, pressure-controlled ventilation, VitalDB

Abstract: Mechanical Power (MP) is a critical determinant of Ventilator-Induced Lung Injury (VILI), yet its real-time calculation in pressure-controlled ventilation (PCV) remains a challenge due to the complexity of existing analytical formulas and the inconsistent availability of ICU sensor data. This study proposes a hybrid deep learning architecture, combining a One-Dimensional Convolutional Neural Network (1D CNN) with Bidirectional Long Short-Term Memory (BiLSTM) layers, to estimate MP from 11 standard respiratory parameters. Utilizing research sources from the high-fidelity database VitalDB, the model was trained and validated against four established MP equations. To ensure clinical robustness, a feature-masking augmentation strategy was implemented to simulate signal inconsistencies and sensor failures. The results demonstrate exceptional predictive accuracy, achieving a peak (Formula presented.) of 0.9994 and a symmetric Mean Absolute Percentage Error (sMAPE) between 3.80% and 4.87% across all target equations. This “sensor-fusion” approach effectively captures both spatial features and temporal dynamics, providing a reliable, real-time decision-support tool for personalized, lung-protective ventilation strategies.

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Issue

Engineering Proceedings, vol. 150, 2026, Switzerland, https://doi.org/10.3390/engproc2026150003

Вид: публикация в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus