A novel method to precisely detect apnea and hypopnea events by airflow and oximetry signals

Huang, Wu; Guo, Bing; Shen, Yan; Tang, Xiangdong

doi:10.1016/j.compbiomed.2017.06.015

Cited by 27 publications

(21 citation statements)

References 35 publications

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“…It is based on the single stimulus in which a number of neurons carries messages through electrochemical process, which is required for decision making. In the real world, the identification and prediction of sleep apnea by deep learning method would facilitate patients with sleep apnea disease [1]. The deep learning model allows to learn from the feature to represent the nature of the data and its patterns.…”

Section: Deep Learning Stepsmentioning

confidence: 99%

“…The type of studies used in our system is type 4 sleep studies, which also refers to continuous single bio-parameter or dual-bio parameter recording. The minimum number of signals that can be used in this type 4 studies is one or two channels such as oxygen saturation and airflow [3][4][5][6][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Normally type 4 studies do not have EEG and EMG signals, so scoring sleep is not possible.…”

Section: Introductionmentioning

confidence: 99%

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Sleep apnea detection using deep learning

Chaw

Kamolphiwong

Wongsritrang³

2019

Teh. glas. (Online)

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Sleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full night polysomnography are time consuming and it requires much space for sleep recording such as sleep lab comparing to type 4 sleep studies. The detection of sleep apnea using deep convolutional neural network model based on SPO2 sensor is the valid alternative for efficient polysomnography and it is portable and cost effective. The total number of samples from SPO2 sensors of 50 patients that is used in this study is 190,000. The performance of the overall accuracy of sleep apnea detection is 91.3085% with the loss rate of 2.3 using cross entropy cost function using deep convolutional neural network.

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Section: Deep Learning Stepsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sleep apnea detection using deep learning

Chaw

Kamolphiwong

Wongsritrang³

2019

Teh. glas. (Online)

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“…For example, in [15] the authors preprocess their data with a high-pass filter and a fast Fourier transformation, to extract several window statistics and train an AdaBoost classifier. Alternative models that have been used include Support Vector Machines [8], K-Nearest Neighbor models [15] and shallow Artificial Neural Networks [7]. Other methods advocate for the use of models that can capture the temporal information between successive windows for classification.…”

Section: Introductionmentioning

confidence: 99%

AI vs Humans for the diagnosis of sleep apnea

Thorey¹,

Hernández²,

Arnal

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Polysomnography (PSG) is the gold standard for diagnosing sleep obstructive apnea (OSA). It allows monitoring of breathing events throughout the night. The detection of these events is usually done by trained sleep experts. However, this task is tedious, highly time-consuming and subject to important inter-scorer variability. In this study, we adapted our state-of-the-art deep learning method for sleep event detection, DOSED, to the detection of sleep breathing events in PSG for the diagnosis of OSA. We used a dataset of 52 PSG recordings with apnea-hypopnea event scoring from 5 trained sleep experts. We assessed the performance of the automatic approach and compared it to the inter-scorer performance for both the diagnosis of OSA severity and, at the microscale, for the detection of single breathing events. We observed that human sleep experts reached an average accuracy of 75% while the automatic approach reached 81% for sleep apnea severity diagnosis. The F1 score for individual event detection was 0.55 for experts and 0.57 for the automatic approach, on average. These results demonstrate that the automatic approach can perform at a sleep expert level for the diagnosis of OSA.

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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%

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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A novel method to precisely detect apnea and hypopnea events by airflow and oximetry signals

Cited by 27 publications

References 35 publications

Sleep apnea detection using deep learning

Sleep apnea detection using deep learning

AI vs Humans for the diagnosis of sleep apnea

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

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