An Adaptive Sleep Apnea Detection Model using Multi Cascaded Atrous based Deep Learning Schemes with Hybrid Artificial Humming Bird Pity Beetle Algorithm

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F23%3A10253237" target="_blank" >RIV/61989100:27230/23:10253237 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10264064" target="_blank" >https://ieeexplore.ieee.org/document/10264064</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3319452" target="_blank" >10.1109/ACCESS.2023.3319452</a>

Result language

angličtina

Original language name

An Adaptive Sleep Apnea Detection Model using Multi Cascaded Atrous based Deep Learning Schemes with Hybrid Artificial Humming Bird Pity Beetle Algorithm

Original language description

Obstructive Sleep Apnea (OSA) is the cessation in breathing that must be identified as early as possible to save the patient&apos;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 APHBA the accuracy of MCA-DLS was improved to 96.4% on average, which is higher than the conventional optimization techniques which are discussed in the result section. Author

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20301 - Mechanical engineering

Project

<a href="/en/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Research Centre of Advanced Mechatronic Systems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

—

Volume of the periodical

2023

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

20

Pages from-to

113114-113133

UT code for WoS article

001092024000001

EID of the result in the Scopus database

2-s2.0-85173032607