Abstract:
BACKGROUND AND OBJECTIVE:Respiratory gating training is a common technique to increase patient proprioception, with the goal of (e.g.) minimizing the effects of organ motion during radiotherapy. In this work, we devise a system based on autoencoders for classification of regular, apnea and unconstrained breathing patterns (i.e. multiclass). METHODS:Our approach is based on morphological analysis of the respiratory signals, using an autoencoder trained on regular breathing. The correlation between the input and output of the autoencoder is used to train and test several classifiers in order to select the best. Our approach is evaluated in a novel real-world respiratory gating biofeedback training dataset and on the Apnea-ECG reference dataset. RESULTS:Accuracies of 95 ± 3.5% and 87 ± 6.6% were obtained for two different datasets, in the classification of breathing and apnea. These results suggest the viability of a generalised model to characterise the breathing patterns under study. CONCLUSIONS:Using autoencoders to learn respiratory gating training patterns allows a data-driven approach to feature extraction, by focusing only on the signal's morphology. The proposed system is prone to be used in real-time and could potentially be transferred to other domains.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Abreu M,Fred A,Valente J,Wang C,Plácido da Silva Hdoi
10.1016/j.cmpb.2020.105675subject
Has Abstractpub_date
2020-10-01 00:00:00pages
105675eissn
0169-2607issn
1872-7565pii
S0169-2607(20)31508-Xjournal_volume
195pub_type
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