E-ISSN 3041-4199
 

Research Article

Online Publishing Date:
25 / 03 / 2024



CAL neural network for precise severe obstructive sleep apnea detection

N. Juber Rahman, P. Nithya Palanisamy.


Abstract
Aim and Background: Obstructive sleep apnea (OSA) presents significant health risks and requires accurate detection for effective intervention. This study introduces the convolutional neural network with artificial neural network with long-term short memory (CAL) neural network, a novel approach that combines convolutional neural network (CNN), artificial neural network (ANN), and long-term short memory (LSTM) techniques to enhance OSA detection. The network leverages spatial feature extraction, pattern recognition, and temporal dependency modeling to improve detection accuracy.
Methods: The proposed CAL neural network undergoes preprocessing, including median filtering for noise removal, before analyzing time-series representations of physiologica
signals such as electroencephalogram, electrocardiogram, and respiratory signals. The CNN component extracts spatial features from raw signals, capturing relevant patterns and relationships among different channels. The output of the CNN is then integrated into an ANN layer to refine learned features and identify complex patterns indicative of severe OSA. LSTM layers are subsequently introduced to capture long-term dependencies in signal dynamics, crucial for detecting subtle variations associated with severe OSA events.
Results: Preliminary results demonstrate the effectiveness of the CAL neural network in detecting severe OSA. By combining CNN, ANN, and LSTM techniques, the network achieves improved accuracy in identifying OSA-related patterns and variations in physiological signals.
Conclusion: The CAL neural network presents a promising approach for OSA detection, offering enhanced accuracy through the synergistic integration of CNN, ANN, and LSTM techniques.

Key words: Artificial Neural Network, Convolutional Neural Network, CAL Neural Network, Median Filtering, Obstructive Sleep Apnea


 
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How to Cite this Article
Pubmed Style

Rahman NJ, Palanisamy PN. CAL neural network for precise severe obstructive sleep apnea detection. J Biol Res Rev. 2024; 1(1): 8-20. doi:10.5455/JBRR.20240208063927


Web Style

Rahman NJ, Palanisamy PN. CAL neural network for precise severe obstructive sleep apnea detection. https://www.scopub.com/jbrr/?mno=189967 [Access: April 20, 2024]. doi:10.5455/JBRR.20240208063927


AMA (American Medical Association) Style

Rahman NJ, Palanisamy PN. CAL neural network for precise severe obstructive sleep apnea detection. J Biol Res Rev. 2024; 1(1): 8-20. doi:10.5455/JBRR.20240208063927



Vancouver/ICMJE Style

Rahman NJ, Palanisamy PN. CAL neural network for precise severe obstructive sleep apnea detection. J Biol Res Rev. (2024), [cited April 20, 2024]; 1(1): 8-20. doi:10.5455/JBRR.20240208063927



Harvard Style

Rahman, N. J. & Palanisamy, . P. N. (2024) CAL neural network for precise severe obstructive sleep apnea detection. J Biol Res Rev, 1 (1), 8-20. doi:10.5455/JBRR.20240208063927



Turabian Style

Rahman, N. Juber, and P. Nithya Palanisamy. 2024. CAL neural network for precise severe obstructive sleep apnea detection. Journal of Biological Research and Reviews, 1 (1), 8-20. doi:10.5455/JBRR.20240208063927



Chicago Style

Rahman, N. Juber, and P. Nithya Palanisamy. "CAL neural network for precise severe obstructive sleep apnea detection." Journal of Biological Research and Reviews 1 (2024), 8-20. doi:10.5455/JBRR.20240208063927



MLA (The Modern Language Association) Style

Rahman, N. Juber, and P. Nithya Palanisamy. "CAL neural network for precise severe obstructive sleep apnea detection." Journal of Biological Research and Reviews 1.1 (2024), 8-20. Print. doi:10.5455/JBRR.20240208063927



APA (American Psychological Association) Style

Rahman, N. J. & Palanisamy, . P. N. (2024) CAL neural network for precise severe obstructive sleep apnea detection. Journal of Biological Research and Reviews, 1 (1), 8-20. doi:10.5455/JBRR.20240208063927