An Advanced Circular Adaptive Search Butterfly Optimization Algorithm for the CNN-based Sleep Apnea Detection Approach

Abstract

The analysis of pulse photoplethysmography (PPG) signals using computerized techniques is a developing field in research. Various effective signal-processing tools have been presented for automatic disease detection systems. Sleep apnea is a syndrome that affects the respiratory system and it commonly occurs due to Oxygen desaturation while sleeping. This paper develops an automatic system to detect sleep apnea from PPG signals. The detection of this syndrome is very important and many approaches were presented to improve the performance. The proposed method improves the classification accuracy through the enhancement of the feature extraction method and using the optimized classifier. As a feature extraction process, Hilbert Huang Transform (HHT) with extrema selection reformed (ESR) Empirical mode decomposition (EMD) is presented in this work. The development of the ESR-EMD system provides a better decomposition of signals and makes feature extraction effective. In addition, the computation time process is reduced as the interpolation is done using the more significant extrema points. Afterward, the feature selection is implemented using fisher discriminant analysis (FDA). An improved CNN classifier with a circular adaptive search butterfly optimization algorithm (CASBOA) is presented for classification. The optimum results obtained using BOA can be increased by employing an adaptive circular search function. This approach can increase the accuracy of the classifier and reduce computational time. The proposed approach is validated in MATLAB with a dataset and the performance metrics are compared with the conventional approaches.

KEYWORDS:

• Bio-medical signals
• Feature extraction
• Neural networks
• Optimization algorithms
• Signal processing

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

J. Geetha

J Geetha is working as associate professor in the Department of Electronics and Communication Engineering at SBM College of Engineering and Technology, Dindigul, TN, India. She completed her bachelor's degree at Odaiyappa College of Engineering, Theni and her Master's degree in the field of applied electronics at SBM College of Engineering and Technology, Dindigul. She is pursuing PhD at Anna University, Chennai and her areas of interest are embedded systems, signal processing, low power VLSI and instrumentation.

J. Benadict Raja

J Benadict Raja is working as associate professor in the Department of Computer Science and Engineering at PSNA College of Engineering and Technology, Dindigul, TN, India. He completed his master's degree and PhD degree at Anna University, Chennai and his research interests include medical image processing and parallel computing. Email: [email protected]