Sleep Arousal Detection Using End-to-End Deep Learning Method Based on Multi-Physiological Signals

Li, Haoqi; Cao, Qineng; Zhong, Yizhou; Pan, Yun

doi:10.22489/cinc.2018.083

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…[15] Deep neural networks with LSTM 0.430 Warrick & Homsi [29] Scattering transform and recurrent neural network 0.375 Li at al. [18] End-to-end deep learning 0.315 Zabihi et al [20] 1D convolutional neural network 0.310 Zabihi et al [30] State distance analysis in phase space 0.190 (*) Submitted outside the time frame of the official stage of the 2018 Physionet challenge. The AUPRC is given for their internal test set, but not the official blind test set of the challenge.…”

Section: Discussionmentioning

confidence: 99%

“…Deep learning methods can model large data-sets better than heuristic algorithms or algorithms based on other machine learning methods such as decision trees or logistic regression that assume an underlying structure to the model. Different deep learning methods are used in the 2018 Physionet challenge to detect non apean/hypopnea sleep arousals [15][16][17][18][19][20]. A good review of all the methods and models that were designed for the challenge as well as the clinical and demographic characteristics of the challenge dataset are also given in [21].…”

Section: Introductionmentioning

confidence: 99%

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SleepNet: automated sleep analysis via dense convolutional neural network using physiological time series

Pourbabaee¹,

Patterson²,

Patterson³

et al. 2019

Physiol. Meas.

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In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep disorders including arousal, apnea and hypopnea using Polysomnography (PSG) measurement channels provided in the 2018 Physionet challenge database. Our model structure is composed of multiple dense convolutional units (DCU) followed by a bidirectional long-short term memory (LSTM) layer followed by a softmax output layer. The sleep events including sleep stages, arousal regions and multiple types of apnea and hypopnea are manually annotated by experts which enables us to train our proposed network using a multi-task learning mechanism. Three binary cross-entropy loss functions corresponding to sleep/wake, target arousal and apneahypopnea/normal detection tasks are summed up to generate our overall network loss function that is optimized using the Adam method. Our model performance was evaluated using two metrics: the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC). To measure our model generalization, 4-fold cross-validation was also performed. For training, our model was applied to full night recording data. Finally, the average AUPRC and AUROC values associated with the arousal detection task were 0.505 and 0.922, respectively on our testing dataset. An ensemble of four models trained on different data folds improved the AUPRC and AUROC to 0.543 and 0.931, respectively. Our proposed algorithm achieved first place in the official stage of the 2018 Physionet challenge for detecting sleep arousals with AUPRC of 0.54 on the blind testing dataset.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SleepNet: automated sleep analysis via dense convolutional neural network using physiological time series

Pourbabaee¹,

Patterson²,

Patterson³

et al. 2019

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…A signifcant relation is indicated by a positive correlation of selected parameters, whereas a negative correlation implies an inverse relationship. Te EEG signal's estimated capabilities include fve frequency bands delta (4.5-5.0 Hz), theta (5.5-8.5 Hz), alpha (5-5.9 Hz), sigma (13-16.5 H), and beta (13-16.5 H) (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Te spectral power of ECG signals was investigated at fve diferent frequencies ranging from 0.33 Hz to 0.4 Hz.…”

Section: Physiological Signal Correlationmentioning

confidence: 99%

“…Warrick and Nabhan Homsi [22] proposed identifying sleep arousal using PSG and recurrent neural networks (RNNs). Deep learning (DL) has recently been widely employed to analyze EEG and ECG signals, including convolutional neural networks (CNNs) for sleep stage scoring [23][24][25][26], and an end-to-end (e2e) deep learning method based on multiphysiological inputs [27]. Tsinalis et al [28] used timefrequency analysis and stacked sparse auto-encoders (AEs) to create an autonomous sleep stage grading system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning

Badiei

Meshgini

Rezaee

2023

Computational Intelligence and Neuroscience

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The vast majority of sleep disturbances are caused by various types of sleep arousal. To diagnose sleep disorders and prevent health problems such as cardiovascular disease and cognitive impairment, sleep arousals must be accurately detected. Consequently, sleep specialists must spend considerable time and effort analyzing polysomnography (PSG) recordings to determine the level of arousal during sleep. The development of an automated sleep arousal detection system based on PSG would considerably benefit clinicians. We quantify the EEG-ECG by using Lyapunov exponents, fractals, and wavelet transforms to identify sleep stages and arousal disorders. In this paper, an efficient hybrid-learning method is introduced for the first time to detect and assess arousal incidents. Modified drone squadron optimization (mDSO) algorithm is used to optimize the support vector machine (SVM) with radial basis function (RBF) kernel. EEG-ECG signals are preprocessed samples from the SHHS sleep dataset and the PhysioBank challenge 2018. In comparison to other traditional methods for identifying sleep disorders, our physiological signals correlation innovation is much better than similar approaches. Based on the proposed model, the average error rate was less than 2%–7%, respectively, for two-class and four-class issues. Additionally, the proper classification of the five sleep stages is determined to be accurate 92.3% of the time. In clinical trials of sleep disorders, the hybrid-learning model technique based on EEG-ECG signal correlation features is effective in detecting arousals.

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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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Sleep Arousal Detection Using End-to-End Deep Learning Method Based on Multi-Physiological Signals

Cited by 13 publications

References 16 publications

SleepNet: automated sleep analysis via dense convolutional neural network using physiological time series

SleepNet: automated sleep analysis via dense convolutional neural network using physiological time series

A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning

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

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