A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

Abstract

Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night’s sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.

Aerial Separation and Receiver Arrangements on Identifying Lung Syndromes Using the Artificial Neural Network

“…ere are two types of lung cancer, namely, small cell lung cancer and non-small cell lung cancer [1][2][3].…”

“…The most common types of lung cancer are lung nodules, non-small-cell lung cancer, and mesothelioma. There are two types of lung cancer, namely, small cell lung cancer and non-small cell lung cancer [ 1 – 3 ].…”

“…Jeya Jothi et al [ 1 ] have implemented a comprehensive review of computational models for obstructive sleep apnea detection in biomedical applications. This paper proposed that diseases such as obstructive sleep apnea (OSA) are caused in the form of depression and mental disorder; therefore, this paper implements the various methods of preprocessing, feature extraction, segmentation, and classification techniques.…”

On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning

“…consuming method, which limits its global coverage to a large extent and leads to delayed diagnosis and increased waiting lists [8]. Hence, many researchers have committed to find a convenient alternative method to PSG [9], [10].…”

“…To date, epoch-wise OSA detectors based on single-lead ECG recordings have reported the greatest diagnostic accuracy when compared to other physiological signals, also providing a more convenient opportunity for the design and development of single-sensor wearable technology [8], [10]. Furthermore, beyond ECG morphology analyses from statistical [13], [14] and transformed domains [15], [16], the ECG signal also allows to easily obtain heart rate variability (HRV) and then study activation of the sympathetic nervous system, which is the main drive of breathing control and a major contributor to the metabolic dysfunction and elevated blood pressure noticed in OSA presence [8]. Actually, most of the recently proposed epoch-wise OSA detectors are based on the combination of HRV features through common machine learning (ML) classifiers.…”