Morphological autoencoders for apnea detection in respiratory gating radiotherapy. (October 2020)

• Record Type: Journal Article
• Authors: Abreu, Mariana; Fred, Ana; Valente, João; Wang, Chen; Plácido da Silva, Hugo
• Is Part Of: Computer methods and programs in biomedicine. Volume 195(2020)
• Publication Date: 2020-10
• Subjects: Artificial neural networks -- Respiratory gating -- Apnea detection -- Machine learning -- Signal processing; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105675
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Recognition of apnea and regular breathing patterns in respiratory gating training and a reference dataset. A morphological analysis was followed through autoencoders, where supervised learning was used to evaluate input-output correlation of the autoencoder. This morphological approach led to promising results in both datasets, hence avoiding feature engineering and enhancing transferability to other time series analysis. 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.