Journal
ENTROPY
Volume 23, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/e23121661
Keywords
cross-entropy; machine learning; convolutional neural network; impedance cardiography; data fusion; time-series classification; aortic pathology; aortic dissection
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Funding
- Lead Project Mechanics, Modeling, and Simulation of Aortic Dissection of Graz University of Technology
- Graz University of Technology
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Aortic dissection, a specific pathology of the aorta, has low incidence but high mortality rates. Early identification and treatment are crucial for patient survival. Using impedance cardiography signals for detection, a trained neural network shows high specificity and sensitivity, with better accuracy for small false lumens.
An aortic dissection, a particular aortic pathology, occurs when blood pushes through a tear between the layers of the aorta and forms a so-called false lumen. Aortic dissection has a low incidence compared to other diseases, but a relatively high mortality that increases with disease progression. An early identification and treatment increases patients' chances of survival. State-of-the-art medical imaging techniques have several disadvantages; therefore, we propose the detection of aortic dissections through their signatures in impedance cardiography signals. These signatures arise due to pathological blood flow characteristics and a blood conductivity that strongly depends on the flow field, i.e., the proposed method is, in principle, applicable to any aortic pathology that changes the blood flow characteristics. For the signal classification, we trained a convolutional neural network (CNN) with artificial impedance cardiography data based on a simulation model for a healthy virtual patient and a virtual patient with an aortic dissection. The network architecture was tailored to a multi-sensor, multi-channel time-series classification with a categorical cross-entropy loss function as the training objective. The trained network typically yielded a specificity of (93.9 & PLUSMN;0.1)% and a sensitivity of (97.5 & PLUSMN;0.1)%. A study of the accuracy as a function of the size of an aortic dissection yielded better results for a small false lumen with larger noise, which emphasizes the question of the feasibility of detecting aortic dissections in an early state.
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