Obstructive sleep apnea event prediction using recurrence plots and convolutional neural networks (RP-CNNs) from polysomnographic signals. (August 2021)

• Record Type: Journal Article
• Authors: Taghizadegan, Yashar; Jafarnia Dabanloo, Nader; Maghooli, Keivan; Sheikhani, Ali
• Is Part Of: Biomedical signal processing and control. Volume 69(2021)
• Publication Date: 2021-08
• Subjects: Prediction -- Obstructive sleep apnea (OSA) -- Polysomnography -- Recurrence plots (RPs) -- Convolutional neural network (CNN) -- Fusion -- Weighted majority voting (WMV); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102928
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Representation of dynamic behavior of EEG, ECG and respiration channels of OSA patients using the recurrence plot (RP) were used to predict OSA events. A novel system based on fusion of Recurrence Plot (RP), pre-trained convolutional neural networks and majority voting method was proposed to predict OSA. Fusion of fine-tuned ShuffleNets on recurrence plots from EEG, ECG and respiration channels improves prediction rate of OSA compare to single channels. Abstract: The prediction of Obstructive Sleep Apnea (OSA) through common polysomnographic signals before stop breathing triggers the ventilation-aided machines such as Continuous Positive Airway Pressure (CPAP). In this paper, a novel schema is proposed based on the representation of the dynamical behavior of polysomnographic signals. This procedure is accomplished using a combination of the Recurrence Plots (RPs) and Convolutional Neural Networks (CNNs), called RP-CNNs. In this regard, the OSA events of 30, 60, 90, and 120 s are predicted before the occurrence. The first phase was to create RP images via Electroencephalogram (EEG), Electrocardiogram (ECG), and respiration signals at a single level. Then, the RP images were independently fed into two fast and robust pre-trained CNNs, naming ResNet-18 and ShuffleNet. Thus, the networks were fine-tuned, and the mentioned events were classified. In the second phase, the classification results were fused using the Weighted Majority Voting (WMV) method to make the final decision. Finally, subject-dependent and subject-independent evaluation criteria were utilized for the MIT-BIH polysomnographic and Dublin sleep apnea databases. The RP-ShuffleNet and 10-fold cross-validation were employed to attain the highest average accuracy and Area Under the Curve (AUC) through 30-second intervals before the OSA events at fusion-level in MIT-BIH polysomnographic and Dublin sleep apnea databases. The achieved results were 90.72%, 0.8937, 90.45%, and 0.9010, respectively. Predicting the OSA events using representation of the dynamical behavior of polysomnographic signals and the fusion of results of the fine-tuned CNNs have been led to the enhancement of the results compared to the state-of-the-art studies.