In Physiological measurement ; h5-index 36.0
OBJECTIVE : The cardiac-related component in the chest electrical impedance tomography (EIT) measurement is of potential value to pulmonary perfusion monitoring and cardiac function measurement. In the spontaneous breathing case, cardiac-related signals experience serious interference from ventilation-related signals. Traditional cardiac-related signal separation methods are usually based on certain features of signals. To further improve the separation accuracy, more comprehensive features of the signals should be exploited.
APPROACH : We propose an unsupervised deep learning method called Deep Feature-Domain Matching (DFDM), which exploits the feature-domain similarity of the desired signals and the breath-holding signals. This method is characterized by two sub-steps. In the first step, a novel Siamese network is designed and trained to learn common features of breath-holding signals; in the second step, the Siamese network is used as a feature-matching constraint between the separated signals and the breath-holding signals.
MAIN RESULTS : The method is first tested using synthetic data, and the results show satisfactory separation accuracy. The method is then tested using the data of three patients with pulmonary embolism, and the consistency between the separated images and the radionuclide perfusion scanning images is checked qualitatively.
SIGNIFICANCE : The method uses a light-weight convolutional neural network for fast network training and inference. It is a potential method for dynamic cardiac-related signal separation in clinical settings.
Zhang Ke, Li Maokun, Liang Haiqing, Wang Juan, Yang Fan, Xu Shenheng, Abubakar Aria
cardiac-related signal, deep metric learning, electrical impedance tomography, feature-domain matching, pulmonary perfusion imaging, signal separation