| dc.description.abstract | Obstructive sleep apnea, the most prevalent
type, is characterized by abnormal breathing patterns or
intervals of difficulty breathing while sleeping. The most
frequent ailment is obstructive sleep apnea (OSA). All
ages are affected, however older persons are the most
typically impacted. The regular sleep cycle is
dramatically altered by OSA, which results in numerous
heart-related problems. The traditional way of
diagnosing sleep problems is polysomnography (PSG),
although over the past few decades, various alternatives
have been offered to replace traditional approaches due
to their complexity and time commitment. This study
proposes a deep learning-based obstructive sleep apnea
detection system that uses the power of convolutional
neural networks (CNN), artificial neural networks
(ANN), and logistic regression algorithms to detect sleep
apnea patterns from electroencephalogram (EEG)
signals.The hybrid classifier technique used by the system
successfully recoversspatial and temporal
information from EEG data, increasing the precision and
efficacy of sleep apnea detection. The study's
methodology involves data collection, preprocessing,
feature extraction, and model training using a labeled
dataset of EEG signals from patients with obstructive
sleep apnea. The deep learning-based classifier's
performance is assessed using a different test dataset to
determine accuracy, sensitivity, specificity, and area
under the curve (AUC). The results show that the
suggested method surpasses existing state-of- the-art
techniques in identifying sleep apnea, giving a more
accurate and efficient diagnosis. However, the system's
dependability is strongly dependent on the correctness
and completeness of EEG data, and more validation with
varied datasets is required to establish its generalization
abilities. | en_US |
