Sleep Arousal Detection From Polysomnography Using the Scattering Transform and Recurrent Neural Networks

Warrick, Philip; Homsi, Masun Nabhan

doi:10.22489/cinc.2018.368

Cited by 16 publications

(16 citation statements)

References 8 publications

(12 reference statements)

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“…The loss weighting was fixed to one for all other classes. The work produced an AUPRC of 0.50, which gave a substantial increase of 0.14 over the previous approach [72] submitted for the 2018 PhysioNet Challenge.…”

Section: Microarousal Detection With the Cnn And Lstmmentioning

confidence: 90%

“…Deep learning methods possess the strong capability to learn complex features by directly applying them to raw data without extracting any hand-crafted features. Only recently have researchers begun to show a preference for deep learning methods, such as CNN [68][69][70][71], ResNet [48], the Siamese architecture network [70], RNN, and LSTM [59,72,73], over traditional machine learning methods in arousal detection.…”

Section: Microarousal Detection With Deep Learning Methodsmentioning

confidence: 99%

“…Bi-LSTM depends on the output layer not only at the current time but also at the next moment. Some recent studies used RNN and LSTM networks to analyze PSG time series [59,72,73].…”

Section: Microarousal Detection With Rnn and Long Short-term Memory (Lstm)mentioning

confidence: 99%

“…Warrick et al [72] discussed a model in which PSG signals were presented to a scattering transform [96] representation layer, and then fed into the three layers of LSTM for sequence learning. A ten-fold cross-validation technique was used, each fold being partitioned as training (90%), validation (10%) and testing (10%) sets.…”

Section: Microarousal Detection With Rnn and Long Short-term Memory (Lstm)mentioning

confidence: 99%

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A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Qian

Qiu

et al. 2021

Brain Sciences

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Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.

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Section: Microarousal Detection With the Cnn And Lstmmentioning

confidence: 90%

Section: Microarousal Detection With Deep Learning Methodsmentioning

confidence: 99%

“…Bi-LSTM depends on the output layer not only at the current time but also at the next moment. Some recent studies used RNN and LSTM networks to analyze PSG time series [59,72,73].…”

Section: Microarousal Detection With Rnn and Long Short-term Memory (Lstm)mentioning

confidence: 99%

Section: Microarousal Detection With Rnn and Long Short-term Memory (Lstm)mentioning

confidence: 99%

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A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Qian

Qiu

et al. 2021

Brain Sciences

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show abstract

“…However, when the authors utilized 68 features in a

feature matrix for a three-layer neural network, the resulting AUPRC was 42.00%. In [ 19 ], the authors proposed using the scattering transform for raw PSG signals. For each signal, 36 coefficients were obtained and fed into a sequence learning machine, which was implemented by a three-layer LSTM network.…”

Section: Related Workmentioning

confidence: 99%

Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning

Chien

Tsao

2021

Sensors

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Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.

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Deep convolutional architecture‐based hybrid learning for sleep arousal events detection through single‐lead EEG signals

et al. 2023

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Introduction: Detecting arousal events during sleep is a challenging, time-consuming, and costly process that requires neurology knowledge. Even though similar automated systems detect sleep stages exclusively, early detection of sleep events can assist in identifying neuropathology progression.Methods: An efficient hybrid deep learning method to identify and evaluate arousal events is presented in this paper using only single-lead electroencephalography (EEG) signals for the first time. Using the proposed architecture, which incorporates Inception-ResNet-v2 learning transfer models and optimized support vector machine (SVM) with the radial basis function (RBF) kernel, it is possible to classify with a minimum error level of less than 8%. In addition to maintaining accuracy, the Inception module and ResNet have led to significant reductions in computational complexity for the detection of arousal events in EEG signals. Moreover, in order to improve the classification performance of the SVM, the grey wolf algorithm (GWO) has optimized its kernel parameters.Results: This method has been validated using pre-processed samples from the 2018 Challenge Physiobank sleep dataset. In addition to reducing computational complexity, the results of this method show that different parts of feature extraction and classification are effective at identifying sleep disorders. The proposed model detects sleep arousal events with an average accuracy of 93.82%. With the lead present in the identification, the method becomes less aggressive in recording people's EEG signals. Conclusion:According to this study, the suggested strategy is effective in detecting arousals in sleep disorder clinical trials and may be used in sleep disorder detection clinics.

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Sleep Arousal Detection From Polysomnography Using the Scattering Transform and Recurrent Neural Networks

Cited by 16 publications

References 8 publications

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning

Deep convolutional architecture‐based hybrid learning for sleep arousal events detection through single‐lead EEG signals

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