This study investigates the efficiency of CNN-DRNN hybrid classifiers in detecting sleep apnea using electrocardiogram (ECG) signals. Various CNN models were evaluated, including AlexNet, VGG16, VGG19, and ZFNet, along with DRNN models such as LSTM, GRU, and BiLSTM. These models were compared with and without the application of swarm intelligence optimizers, namely the Honey Badger Algorithm (HBA) and Grey Wolf Optimizer (GWO), for optimizing hyperparameter values. The results demonstrated that the AlexNet-GRU hybrid model achieved the best performance after applying both optimizers, with an accuracy of 95%, a detection rate of 97.61%, and an F-Score of 93.37%.This research also explores the challenges of hyperparameter optimization in deep learning models using swarm intelligence-based optimizers. These optimizers, inspired by natural behaviors, facilitate problem-solving through intelligent distribution, indirect interactions among agents, and simplification of complex processes. Additionally, the findings revealed that HBA outperformed GWO in determining optimal hyperparameter values, leading to enhanced model performance. Overall, the study highlights the potential of integrating deep learning models with swarm intelligence optimizers to improve sleep apnea detection.