Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification

Lakhan, Payongkit; Ditthapron, Apiwat; Banluesombatkul, Nannapas; Wilaiprasitporn, Theerawit

doi:10.1109/tencon.2018.8650491

Cited by 29 publications

(30 citation statements)

References 22 publications

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“…A recent comprehensive survey showed that the vast majority of deep learning methods for sleep breathing disorders have been devoted to

signals [ 46 ]. Few studies considered single channel respiration inputs for deep learning apnea detection models [ 58 , 59 , 60 , 61 ]. Convolutional neural networks were used in [ 58 , 59 , 61 ] while [ 60 ] used human-engineered features as an input to a deeply stacked feed-forward neural network but none of them have evaluated recurrent neural networks.…”

Section: Discussionmentioning

confidence: 99%

“…Few studies considered single channel respiration inputs for deep learning apnea detection models [ 58 , 59 , 60 , 61 ]. Convolutional neural networks were used in [ 58 , 59 , 61 ] while [ 60 ] used human-engineered features as an input to a deeply stacked feed-forward neural network but none of them have evaluated recurrent neural networks. Although CNNs are widely used in deep learning methods, they require very high computational power as opposed to RNNs and are also designed to work with images unlike RNNs that are fundamentally used for signals with temporal dependencies.…”

Section: Discussionmentioning

confidence: 99%

“…Although CNNs are widely used in deep learning methods, they require very high computational power as opposed to RNNs and are also designed to work with images unlike RNNs that are fundamentally used for signals with temporal dependencies. The work of [ 58 , 59 , 60 ] considered only the FlowTh signal but did not consider NPRE and ABD signals. The work of [ 61 ] considered only the NPRE signal and did not evaluate other respiration signals.…”

Section: Discussionmentioning

confidence: 99%

“…This includes the oronasal/nasal airflow together with abdomen and thoracic plethysmography [ 54 ] including SpO

[ 55 , 56 ] or

[ 57 ]. Very few studies considered single channel respiration signals [ 58 , 59 , 60 , 61 ]. Although single channel respiratory methods showed promising preliminary results, they only employed CNNs and did not evaluate recurrent neural networks.…”

Section: Background and Problem Statementmentioning

confidence: 99%

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

ElMoaqet

Eid

Glos

et al. 2020

Sensors

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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.

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“…A recent comprehensive survey showed that the vast majority of deep learning methods for sleep breathing disorders have been devoted to

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…This includes the oronasal/nasal airflow together with abdomen and thoracic plethysmography [ 54 ] including SpO

[ 55 , 56 ] or

Section: Background and Problem Statementmentioning

confidence: 99%

See 2 more Smart Citations

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

ElMoaqet

Eid

Glos

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…PSG can only be used in specialized clinics or hospitals under constant medical supervision to collect physiological signals from patients with sleep disorders, and then use professional sleep experts to diagnose patients who are sleep disorders based on physiological signals detected by PSG [7] This means that patients must go to specialized medical institutions, which will inevitably increase the burden on patients. Moreover, PSG requires the integration of many sensors on the human body, which is considered invasive, so PSG screening can interfere with sleep [8]. In addition, PSG is costly and time consuming, plus the need for professional sleep experts.…”

Section: Introductionmentioning

confidence: 99%

OSA Patient Monitoring System Based on the Internet of Things Framework

Cai¹,

Le²,

Ruan³

et al. 2020

Preprint

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This paper presents an OSA patient interactive monitoring system based on the Internet of Things (IoT) framework. This system allows OSA patients to get timely rescue when they are sleepy outside. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for the rescue. At the same time, if a friend helps the OSA patients to call the doctor, the friend can also report the patient's condition in time. This system uses the popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer is the network layer that transmits the collected physiological signals to the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature rescue interactive platform of Beidou. Since the GPS indicator has not included the communication satellite, So it has no SMS function. OSA patients can only passively wait for a rescue. Moreover, due to the lack of satellites in Asia and the insufficient density of the ground-enhanced system, the positioning error of OSA patients is large. The Beidou system developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground-based augmentation system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in time while waiting for a rescue. After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Internet of Things framework and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the cell phone base station signal coverage is relatively weak. The satellite signal is well covered, plus the SMS function of the Beidou indicator. Therefore, the system can be used to provide timely patient progress and provide data support for the medical rescue team to provide a more accurate rescue plan. After a comparative trial, the rescue rate of OSA patients using the detection device of this system was increased by 15 percentage points compared with the rescue rate using only GPS satellite phones.

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Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Moridian

Shoeibi

Khodatars

et al. 2022

WIREs Data Min & Knowl

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Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining

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Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification

Cited by 29 publications

References 22 publications

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

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

OSA Patient Monitoring System Based on the Internet of Things Framework

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

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