Comparison of ALEXNET and GOOGLENET convolutional neural network models to detect obstructive sleep apnea using single-channel electrocardiogram

Obstructive Sleep apnea (OSA) is a type of sleep disorder caused due to respiratory collapse during sleep. This sleep disorder generally goes undiagnosed and neglected. Severe OSA may cause arrhythmia, sudden death, high blood pressure, and other cardiac anomalies. Polysomnography (PSG) is the most popular gold standard used by many researchers to detect OSA. PSG required a well-equipped sleep laboratory and skilled persons to record multi- channel signals to detect OSA. PSG is a complex and expensive method and hence motivated to conduct the research using single-channel electrocardiogram (ECG). An automatic detection method of OSA using single-channel ECG in Convolutional Neural Network (CNN) takes less computing time as feature engineering does not require. This paper focuses on the automatic detection of OSA using ECG with two different deep CNN architectures AlexNet and GoogLeNet transfer learning. The apnea ECG datasheet is used for assessing the method proposed. The state of art using deep learning models are applied to single-channel ECG data. The GoogLeNet architecture is more complex and achieves 100% accuracy whereas AlexNet architecture shows 99.7% accuracy to detect OSA. The proposed work is applied to physionet apnea ECG online data which leads to an overfitting problem that can be resolved using clinical data to further enhance the robustness of the model.