An adaptive sleep apnea detection model using multi cascaded atrous-based deep learning schemes with hybrid artificial humming bird pity beetle algorithm

Abstract

Obstructive Sleep Apnea (OSA) is the cessation in breathing that must be identified as early as possible to save the patient’s life. Apart from physical diagnosis, a deep learning model can serve the purpose of detecting the apnea swiftly. The detection largely depends upon biological signals such as ECG, EEG, EMG, etc. Because of the high dimensionality nature of the bio signals, feature extraction is very critical in detecting sleep apnea. Many such feature extraction models were fragile to resolve the complexity issue and failed to reduce the non-robustness nature. To surmount all these issues, a novel adaptive deep learning-based model is designed for detecting the sleep apnea. Here two feature sets have been extracted from the ECG signals: Spectral features through Short Term Fourier Transform (STFT) and QRS analysis followed by an auto encoder to extract the deep temporal features. The novel Artificial Hummingbird Pity Beetle Algorithm (AHPBA) is proposed to choose the optimal features and weight parameters, which assists in concatenation of the two feature sets. Then these fused features were given into Multi Cascaded Atrous based Deep Learning Schemes (MCA-DLS) for classification purpose, then it is further optimized by AHPBA by maximizing the variance. MCA-DLS have performed well compared to classifying the signals individually using One Dimensional Convolutional Neural Networks (1DCNN), Long Short-Term Memory (LSTM) and Deep Neural Networks (DNN) as the average accuracy of MCA-DLS is 94.51% whereas the other three provides an average accuracy of 90.83%, 91.98%, and 93.25% respectively for the considered datasets. By using AHPBA the average accuracy of MCA-DLS was enhanced to 96.4%, which is higher than the conventional optimization techniques which are discussed in the result section.