<scp>OSAG</scp> ‐Net: Toward Accurate <scp>OSA</scp> Severity Classification Through Deep Recurrent Learning and Self‐Feature Controllable‐Black Window Optimization

ABSTRACT Obstructive sleep apnea (OSA) becomes a sleep disease caused by recurrent cessation of breathing during sleep; it also leads to various health complications. Despite the availability of diagnostic methods, there are challenges in accurately identifying and classifying OSA severity. This research addresses the need of an efficient and reliable automated system for OSA detection using deep learning techniques. Existing problems include the complexity of OSA diagnosis, reliance on manual scoring, and variability in interpretation. The proposed OSA grading network (OSAG‐Net) encompasses several steps: preprocessing of raw Electrocardiogram (ECG) data to extract relevant features, application of self‐feature controllable‐black window optimization (SFC‐BWO) for feature selection to enhance classification performance, and utilization of bidirectional gated recurrent neural network (BGRNN) architecture with recurrent neural networks (RNN) and bidirectional gated recurrent units (Bi‐GRU) for OSA severity classification. Preprocessing involves filtering noise and artifacts from ECG signals, followed by segmenting data into smaller windows to extract informative features. The SFC‐BWO technique optimally selects the features by iteratively refining feature subsets based on classification performance, effectively reducing dimensionality and enhancing model interpretability. The RNN architecture with Bi‐GRU units is employed to capture temporal dependencies of sequential data, such as ECG recordings, enabling more accurate classification of OSA severity levels. Finally, the performance of the system is validated with different metrics. Hence, the proposed OSAG‐Net model achieves a high accuracy value of more than 4.77% of SVM, 3.67% compared to Grad‐CAM, and 2.54% of CNN‐LSTM, respectively. This results in improvement in the system proves that it rapidly and effectively diagnoses the disease and treats the patients accordingly.