Automatic Diagnosis of Obstructive Sleep Apnea/Hypopnea Events Using Respiratory Signals

Aydoğan, Osman; Öter, Ali; Güney, Kerïm; Kıymık, M. Kemal; Tunçel, Deniz

doi:10.1007/s10916-016-0624-0

Cited by 6 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Blanco et al introduce a novel method to detect OSA based on patient's voices [29] on the manually collected dataset. In [30], Aydogan et al performs visual scoring of 74 patients using two methods, namely morphological filter and ANN-based to diagnose OSA. They analyze the scoring success of both methods and obtain an average accuracy of 88.33% and 87.28% for ANN and morphological filter-based methods, respectively.…”

Section: Related Workmentioning

confidence: 99%

Obstrüktif Uyku Apnesi Tanıma için Öznitelik Seçimi

Sert

2019

Bilişim Teknolojileri Dergisi

View full text Add to dashboard Cite

Obstructive sleep apnea (OSA) is a kind of sleep disorder and it is described by breathing irregularity during sleep. This disorder may lead to long-term consequences, such as sleep related irregularities and/or cardiovascular diseases. This paper proposes a multimodal and feature selection-based processing pipeline to detect OSA as a computerbased alternative way to clinical polysomnography (PSG) method. In the proposed method, the oxygen saturation (SpO2) and the electrocardiogram (ECG) signals are fused at the feature-level for the classification. Five feature selection methods, namely Relieff, Chi-Square, Information Gain (IG), Principal Component Analysis (PCA), and Gain Ratio (GR) were applied to the problem to obtain robust features from both signal sources and to reduce the feature dimensionality. The effectiveness of utilized feature selection methods was analyzed using the Support Vector Machine (SVM), k-nearest neighbor (k-NN), and Naive Bayes (NB) classifiers. The experimental results on the real clinical samples from the PhysioNet dataset show that the proposed multimodal and feature selection-based method improves the classification accuracy, significantly.

show abstract

Section: Related Workmentioning

confidence: 99%

Obstrüktif Uyku Apnesi Tanıma için Öznitelik Seçimi

Sert

2019

Bilişim Teknolojileri Dergisi

View full text Add to dashboard Cite

show abstract

“…Other studies conducted this classification relying on the time-domain phase difference [20], instantaneous phase, and phase-locking value of THO and ABD [22]. The information derived from the wavelet analysis of these signals was also evaluated [23][24][25], even combined with wavelet features extracted from AF and/or SpO 2 to automatically score apneic events or classify them according to their origin [26][27][28][29]. All these studies focused on characterizing the THO and ABD behaviors by means of a conventional feature-engineering approach, thus requiring the extraction of suitable features and relying on the potentially limited knowledge on the effects of sleep apnea in overnight THO and ABD signals.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals

Barroso-García,

Fernández-Poyatos,

Sahelices

et al. 2023

Diagnostics

View full text Add to dashboard Cite

The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC) = 0.75 and a root mean square error (RMSE) = 10.33 events/h when estimating the apnea-hypopnea index, and ICC = 0.83 and RMSE = 0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea events.

show abstract