Automatic Respiratory Event Scoring in Obstructive Sleep Apnea Using a Long Short-Term Memory Neural Network

Abstract

The diagnosis of obstructive sleep apnea is based on daytime symptoms and the frequency of respiratory events during the night. The respiratory events are scored manually from polysomnographic recordings, which is time-consuming and expensive. Therefore, automatic scoring methods could considerably improve the efficiency of sleep apnea diagnostics and release the resources currently needed for manual scoring to other areas of sleep medicine.In this study, we trained a long short-term memory neural network for automatic scoring of respiratory events using input signals from peripheral blood oxygen saturation, thermistorairflow, nasal pressure -airflow, and thorax respiratory effort. The signals were extracted from 887 in-lab polysomnography recordings. 787 patients with suspected sleep apnea were used to train the neural network and 100 patients were used as an independent test set.The epoch-wise agreement between manual and automatic neural network scoring was high (88.9%, κ=0.728). In addition, the apnea-hypopnea index (AHI) calculated from the automated scoring was close to the manually determined AHI with a mean absolute error of 3.0 events/hour and an intraclass correlation coefficient of 0.985.The neural network approach for automatic scoring of respiratory events achieved high accuracy and good agreement with manual scoring. The presented neural network could be used for analysis of large research datasets that are unfeasible to score manually, and has potential for clinical use in the future In addition, since the neural network scores individual respiratory events, the automatic scoring can be easily reviewed manually if desired.

An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals

“…Another study that aimed to differentiate between apnea and hypopnea episodes was described by Nikkonen et al [ 40 ]. It also uses LSTM networks to deliver class probabilities, but there is no postprocessing stage.…”

“…The other work is based on a newer database that seems to not be publicly available, which is important when comparing the performance of the two approaches. Mean absolute errors of AHI, HI and AI estimation from [ 40 ] are presented in Table 9 .…”

“…However, the method of obtaining these values is different than in the presented work. In [ 40 ], the event is considered correct when it overlaps with an event labeled by a sleep technician, and in this work every 0.5 s of the recording is taken into account separately in accuracy calculation. In the other paper, average episode start and end time errors for apneas were calculated as 7.0 and 1.2, respectively, and for hypopneas they were 6.7 and 2.6, respectively.…”

“…Due to very similar approach, the results of the proposed technique were compared to those of the LSTM from [ 40 ]. The other work reports higher accuracies in all classes; however, they were not obtained on the same database.…”

Classification of Respiratory States Using Spectrogram with Convolutional Neural Network

“…Research on the diagnosis of apnea using ECG-based electrical signals acquired by PSG (Polysomnography) that can provide various information about human biosignals has long been of interest in the research and industrial fields. As deep learning algorithms have shown significant improvements in detection, recognition, and classification in the areas of signal processing, image processing, etc., recent studies on the detection of the apnea status of humans based on a neural network model-e.g., long short-term memory and deep neural network-have been performed [12,13]. Existing work has focused on the classification of respiratory signals in the frequency domain, and recently, a learningbased classification using an artificial neural network has been proposed [14].…”