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Classification of Electrical Impedance Tomography Data Using Machine Learning
In this article, we present a new approach to promote the use of EIT data and the implementation of new clinical applications for differential diagnosis, with the development of several machine learning models to discriminate between EIT data from healthy and non-healthy subjects.
Inferring respiratory and circulatory parameters from electrical impedance tomography with deep recurrent models
Electrical impedance tomography (EIT) is a noninvasive imaging modality that allows a continuous assessment of changes in regional bioimpedance of different organs.
Detecting and interpreting myocardial infarction using fully convolutional neural networks
Objective: We aim to provide an algorithm for the detection of myocardial infarction that operates directly on ECG data without any preprocessing and to investigate its decision criteria.
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