A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Ramachandran, Anita; Karuppiah, Anupama

doi:10.3390/healthcare9070914

Cited by 48 publications

(37 citation statements)

References 70 publications

(131 reference statements)

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“…Deep learning techniques have been increasingly used to diagnose sleep apnea [ 38 ], including convolutional neural networks, such as was used in our study. As in a large part of neurodiagnostic methods, the problem is the small number of measured subjects (i.e., small data set size).…”

Section: Discussionmentioning

confidence: 99%

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

et al. 2021

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Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the airflow and SpO2 channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively.

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Section: Discussionmentioning

confidence: 99%

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

et al. 2021

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“…On the one hand, these approaches greatly depend on signal quality and background noise. On the other hand, they need a manual design with many specific algorithms, such as the sensitive R-wave detection algorithm [24] . In this study, owing to automatic feature extraction and selection of deep learning models, once segmented breaths were denoised and segmented, we directly used them to train our deep models.…”

Section: Related Workmentioning

confidence: 99%

Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare

Tran-Anh

Nguyen-Trong

et al. 2022

Pervasive and Mobile Computing

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“…The golden standard technique for the diagnosis of OSA is in-lab polysomnography [ 2 , 3 , 4 , 5 , 6 , 7 ], which consists in a series of physiological measurements during sleep that allow characterizing the presence of the pathology. Nevertheless, there are other cheaper and simpler alternatives for the diagnosis of this pathology, such as cardiorespiratory polygraphy; however, it does not collect information on neurophysiological variables [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile

Casal-Guisande

Torres-Durán

Mosteiro-Añón

et al. 2023

IJERPH

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Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient’s health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8–0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.

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A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Cited by 48 publications

References 70 publications

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare

Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile

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