BI - Directional long short-term memory for automatic detection of sleep apnea events based on single channel EEG signal

2022 ◽  
pp. 105211
Author(s):  
Yao Wang ◽  
Zhuangwen Xiao ◽  
Shuaiwen Fang ◽  
Weiming Li ◽  
Jinhai Wang ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Short Term Memory ◽  
Single Channel ◽  
Automatic Detection ◽  
Eeg Signal ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5037
Author(s):  
Hisham ElMoaqet ◽  
Mohammad Eid ◽  
Martin Glos ◽  
Mutaz Ryalat ◽  
Thomas Penzel
Keyword(s):  
Feature Extraction ◽  
Sleep Apnea ◽  
Short Term Memory ◽  
Single Channel ◽  
Automated Detection ◽  
Detection Methods ◽  
Short Term ◽  
Term Memory ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory

Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single respiratory channel inputs. Long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) are investigated to develop the proposed deep RNN model. The proposed framework is evaluated over three respiration signals: Oronasal thermal airflow (FlowTh), nasal pressure (NPRE), and abdominal respiratory inductance plethysmography (ABD). To demonstrate our results, we use polysomnography (PSG) data of 17 patients with obstructive, central, and mixed apnea events. Our results indicate the effectiveness of the proposed framework in automatic extraction for temporal features and automated detection of apneic events over the different respiratory signals considered in this study. Using a deep BiLSTM-based detection model, the NPRE signal achieved the highest overall detection results with true positive rate (sensitivity) = 90.3%, true negative rate (specificity) = 83.7%, and area under receiver operator characteristic curve = 92.4%. The present results contribute a new deep learning approach for automated detection of sleep apnea events from single channel respiration signals that can potentially serve as a helpful and alternative tool for the traditional PSG method.

scholarly journals Accurate detection of sleep apnea with long short-term memory network based on RR interval signals

2021 ◽  
Vol 212 ◽  
pp. 106591
Author(s):  
Oliver Faust ◽  
Ragab Barika ◽  
Alex Shenfield ◽  
Edward J. Ciaccio ◽  
U. Rajendra Acharya
Keyword(s):  
Sleep Apnea ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Rr Interval ◽  
Accurate Detection ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6067
Author(s):  
Hung-Chi Chang ◽  
Hau-Tieng Wu ◽  
Po-Chiun Huang ◽  
Hsi-Pin Ma ◽  
Yu-Lun Lo ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Apnea Hypopnea Index ◽  
Short Term ◽  
Term Memory ◽  
Obstructive Sleep ◽  
Long Short Term Memory

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS; however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO2) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was 5.0±4.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.

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