Bare-bones based honey badger algorithm of CNN for Sleep Apnea detection

Sleep Apnea (SA) is a breathing disorder that many people experience during sleep. Polysomnography is the best way to diagnose SA, but it requires significant time, cost, and effort. A practical and efficient method of diagnosing SA is using a wearable sensor to record Electrocardiography (ECG) signals. Machine learning algorithms can be used to classify SA by extracting features from ECG signals. Recently, deep learning techniques such as Convolutional Neural Network (CNN) have been used to identify features from ECG data automatically. However, the large number of hyperparameters in CNN makes it challenging to perform this task manually. Metaheuristic algorithms such as Honey Badger Algorithm (HBA) have been successfully applied to tune CNN hyperparameters, but it still has issues with premature convergence. To address these issues, the Bare-Bones Honey Badger Algorithm (BBHBA) is proposed as an improved version of HBA. It improves the exploitation potential of solutions, reduces diversity spillover, and maintains solution diversity. The method generates new candidate solutions using Gaussian search equations and an inverse hyperbolic cosine control mechanism. The greedy selection strategy is used to improve the searcher’s capabilities effectively. To validate the proposed deep learning model, the PhysioNet Apnea-ECG database is used. The model achieves an accuracy of 90.92