This work significantly impacts the field of artificial intelligence (AI) by demonstrating the successful integration of advanced techniques and algorithms in biomedical applications, particularly for SA detection using ECG data. By introducing two efficient models that effectively handle both computational time and complexity challenges, this research showcases the potential of AI in addressing real-world healthcare problems. The use of SSSA and TFD features highlights the importance of innovative data processing methods in extracting valuable insights from complex biomedical data. Additionally, the novel SPPCA algorithm illustrates how AI can overcome high-dimensional data challenges, enabling more accurate and efficient analysis. Finally, the implementation and validation of multiple ML models and the development of an MLN-CNN emphasize the versatility of AI techniques in solving diverse problems. Furthermore, the impressive performance metrics achieved by the proposed algorithms, o...

Sleep apnea (SA) is a potentially fatal sleep disorder where breathing regularly pauses and resumes during sleep, which results in regular awakenings. In this work, we introduced two efficient models which were tested on both the handcrafted and the latent features. To preprocess and segment the electrocardiogram (ECG) signals into multiple spectrums, this work uses a unique approach known as sliding singular spectrum analysis (SSSA). Later, we considered four time-frequency domain (TFD) features, such as spectral entropy (SE), signal energy (EN), dominant frequency (DF), and spike rhythmicity (SR) to precisely detect and classify SA from the ECG signals. To cope with the high-dimensional nature of the data, we have proposed a novel algorithm named subpattern-based principal component analysis (SPPCA), which can extract the most prominent features by delimiting the dimensions of the original features. To classify the ECG data, the low-dimensional TFD features were used to train and val...