Comparison of Deep Learning Methods for Sleep Apnea Detection Using Spectrogram-Transformed ECG Signals
Abstract
Sleep apnea is a sleep disorder that occurs when breathing is disturbed, characterized by repeated periods of stopping breathing during sleep. This condition can cause various serious health problems if not treated, such as: high blood pressure, poor quality sleep, and difficulty concentrating. Sufferers often don't realize sleep apnea because it occurs during sleep. Generally, sleep apnea diagnosis is made by interviewing the patient and family to find out common symptoms such as snoring, then confirmed through physical examination and polysomnography (PSG). Since sleep apnea is related to respiratory activity that correlates with changes in cardiac activity, ECG examination during sleep is an alternative for diagnosis. Therefore, this study presents a comparative analysis of deep learning models for detecting sleep apnea from spectrogram-based ECG representations. The raw ECG signal is transformed into a spectrogram and then saved as an image for classification, specifically for normal and abnormal classification. Deep Learning (DL) method is applied for classification of normal ECG and sleep apnea ECG. EfficientNet, MobileNet V2, DenseNet, AlexNet, and VGG16 were used to evaluate the performance of the proposed method and to identify the best-performing model. The evaluation results show that EfficientNet demonstrated the highest performance with an accuracy of 91.01%, precision of 90.70%, recall of 95.76%, and an F1-score of 92.61%. EfficientNet outperformed the other evaluated models in this study. By utilizing a spectrogram-based approach combined with a scalable architecture, the method demonstrates competitive accuracy for sleep apnea detection. Investigating other methods to enhance accuracy remains an interesting topic for future study.
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References
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