New Rule-Based Algorithm for Real-Time Detecting Sleep Apnea and Hypopnea Events Using a Nasal Pressure Signal

Lee, Hyoki; Park, Jonguk; Kim, Hojoong; Lee, Kyoung-Joung

doi:10.1007/s10916-016-0637-8

Cited by 23 publications

(14 citation statements)

References 32 publications

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“…Although the proposed automatic apnea detection method can be used as a solution for sleep obstruction suffering patients, this may require special setups due to the required nine biomedical signal measurements to make such a detection. A more lightweight design is presented in [32]. Here, Lee et al develop a real-time system for the identification of hypopnea and sleep apnea events.…”

Section: Related Workmentioning

confidence: 99%

Obstrüktif Uyku Apnesi Tanıma için Öznitelik Seçimi

Sert

2019

Bilişim Teknolojileri Dergisi

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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.

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Section: Related Workmentioning

confidence: 99%

Obstrüktif Uyku Apnesi Tanıma için Öznitelik Seçimi

Sert

2019

Bilişim Teknolojileri Dergisi

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“…Records with labeling flaws were discarded, as well as examination portions containing artifacts. Interesting approaches were presented by Koley and Dey [ 23 ] and Lee et al [ 24 ]. They report good performance in apnea/hypopnea detection and classification task.…”

Section: Introductionmentioning

confidence: 99%

An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals

Drzazga

Cyganek

2021

Sensors

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One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive. Alternatively, diagnostic doctors can be supported with recordings from the in-home polygraphy sensors. Furthermore, numerous attempts for providing an automated apnea episodes annotation algorithm have been made. Most of them, however, do not distinguish between apnea and hypopnea episodes. In this work, a novel solution for epoch-based annotation problem is presented. Utilizing an architecture based on the long short-term memory (LSTM) networks, the proposed model provides locations of sleep disordered breathing episodes and identifies them as either apnea or hypopnea. To achieve this, special pre- and postprocessing steps have been designed. The obtained labels can be then used for calculation of the respiratory event index (REI), which serves as a disease severity indicator. The input for the model consists of the oronasal airflow along with the thoracic and abdominal respiratory effort signals. Performance of the proposed architecture was verified on the SHHS-1 and PhysioNet Sleep databases, obtaining mean REI classification error of 9.24/10.52 with standard deviation of 11.61/7.92 (SHHS-1/PhysioNet). Normal breathing, hypopnea and apnea differentiation accuracy is assessed on both databases, resulting in the correctly classified samples percentage of 86.42%/84.35%, 49.30%/58.28% and 68.20%/69.50% for normal breathing, hypopnea and apnea classes, respectively. Overall accuracies are 80.66%/82.04%. Additionally, the effect of wake periods is investigated. The results show that the proposed model can be successfully used for both episode classification and REI estimation tasks.

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“…However, many other diseases except SAHS also affect ECG. Hence, nasal flow (NF) [3][4][5][6], arterial blood oxygen saturation (SpO 2 ) [7], snoring [8], or a combination of these signals [9,10] have been adopted more recently. Gutierrez et al [4] used the overall features of NF for the diagnosis of SAHS severity.…”

Section: Introductionmentioning

confidence: 99%

“…Xie et al [10] utilized a combination of classifiers to achieve realtime detection of SAHS based on ECG and SpO 2 . All the above studies can be roughly divided into two categories: those that predict the AH index (AHI) based on the detection of AH events [2,3,5,7,[9][10][11], and those that predict AHI based on the overall signal features [1,4,6,8,12,13]. The latter approach cannot provide time information for each AH event, whereas most studies in the former [2,7,10,11] only involve a 60-s segment identification which may not be accurate for predicting the segments containing multiple AH events and may lead to errors in the estimation of AHI.…”

Section: Introductionmentioning

confidence: 99%

“…The latter approach cannot provide time information for each AH event, whereas most studies in the former [2,7,10,11] only involve a 60-s segment identification which may not be accurate for predicting the segments containing multiple AH events and may lead to errors in the estimation of AHI. On the other hand, the methods mentioned above include rule-based [5,7,9], SVM [2,10,11], and supervised neural network [3,11], which require a large number of hyperparameters to be set by experience. Therefore, we utilized random forests composed of classification and regression trees (CARTs) based on morphological features extracted from NF and SpO 2 for AH events detection.…”

Section: Introductionmentioning

confidence: 99%

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Cascading detection model for prediction of apnea-hypopnea events based on nasal flow and arterial blood oxygen saturation

Deng

Sun

et al. 2019

Sleep Breath

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Purpose Sleep apnea and hypopnea syndrome (SAHS) seriously affects sleep quality. In recent years, much research has focused on the detection of SAHS using various physiological signals and algorithms. The purpose of this study is to find an efficient model for detection of apnea-hypopnea events based on nasal flow and SpO 2 signals. Methods A 60-s detector and a 10-s detector were cascaded for precise detection of apnea-hypopnea (AH) events. Random forests were adopted for classification of data segments based on morphological features extracted from nasal flow and arterial blood oxygen saturation (SpO 2). Then the segments' classification results were fed into an event detector to locate the start and end time of every AH event and predict the AH index (AHI). Results A retrospective study of 24 subjects' polysomnography recordings was conducted. According to segment analysis, the cascading detection model reached an accuracy of 88.3%. While Pearson's correlation coefficient between estimated AHI and reference AHI was 0.99, in the diagnosis of SAHS severity, the proposed method exhibited a performance with Cohen's kappa coefficient of 0.76. Conclusions The cascading detection model is able to detect AH events and provide an estimate of AHI. The results indicate that it has the potential to be a useful tool for SAHS diagnosis.

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New Rule-Based Algorithm for Real-Time Detecting Sleep Apnea and Hypopnea Events Using a Nasal Pressure Signal

Cited by 23 publications

References 32 publications

Obstrüktif Uyku Apnesi Tanıma için Öznitelik Seçimi

Obstrüktif Uyku Apnesi Tanıma için Öznitelik Seçimi

An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals

Cascading detection model for prediction of apnea-hypopnea events based on nasal flow and arterial blood oxygen saturation

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