Spatio-Temporal Classification of Lung Ventilation Patterns Using 3D EIT Images: A General Approach for Individualized Lung Function Evaluation

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 1; pp. 367 - 378
• Main Authors: Chen, Shuzhe, Li, Li, Lin, Zhichao, Zhang, Ke, Gong, Ying, Wang, Lu, Wu, Xu, Li, Maokun, Song, Yuanlin, Yang, Fan, Xu, Shenheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Artificial neural networks; Chronic obstructive pulmonary disease; Classification; Computed tomography; Electrical impedance; Electrical impedance tomography; Electrical impedance tomography (EIT); Evaluation; Feasibility; Feature maps; Lung; lung ventilation classification; Lungs; Medical imaging; Model accuracy; Neural networks; Predictive models; pulmonary function test (PFT); Pulmonary functions; Reliability analysis; Respiratory function; Spatial distribution; Temporal variations; Three-dimensional displays; variational autoencoder (VAE); Ventilation
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2023.3328343

The Pulmonary Function Test (PFT) is a widely utilized and rigorous classification test for evaluating lung function, serving as a comprehensive diagnostic tool for lung conditions. Meanwhile, Electrical Impedance Tomography (EIT) is a rapidly advancing clinical technique that visualizes conductivity distribution induced by ventilation. EIT provides additional spatial and temporal information on lung ventilation beyond traditional PFT. However, relying solely on conventional isolated interpretations of PFT results and EIT images overlooks the continuous dynamic aspects of lung ventilation. This study aims to classify lung ventilation patterns by extracting spatial and temporal features from the 3D EIT image series. The study uses a Variational Autoencoder (VAE) with a MultiRes block to compress the spatial distribution in a 3D image into a one-dimensional vector. These vectors are then stacked to create a feature map for the exhibition of temporal features. A simple convolutional neural network is used for classification. Data from 137 subjects were utilized for the training phase. Initially, the model underwent validation through a leave-one-out cross-validation process. During this validation, the model achieved an accuracy and sensitivity of 0.96 and 1.00, respectively, with an f1-score of 0.98 when identifying the normal subjects. To assess pipeline reliability and feasibility, we tested it on 9 newly recruited subjects, with accurate ventilation mode predictions for 8 out of 9. In addition, we included 2D EIT results for comparison and conducted ablation experiments to validate the effectiveness of the VAE. The study demonstrates the potential of using image series for lung ventilation mode classification, providing a feasible method for patient prescreening and presenting an alternative form of PFT.