Detection Of Sleep Arousals Research Articles

Detection of sleep arousal from STFT-based instantaneous features of single channel EEG signal.

Sleep arousal, a frequent interruption in sleep with complete or partial wakefulness from sleep, may indicate a breathing disorder, neurological disorder, or sleep-related disorders. These phenomena necessitate the detection of sleep arousals. Uses of deep learning methods to detect features inhibits the scope to understand the specific distinctive nature of the signals and reduces the interpretability of the model. To evade these inconsistencies and to improve the classification performance of the sleep arousal detection model, a model has been proposed in this study on the prospect of understandable features that are useful in detecting sleep arousals. 
Approach: Time-frequency analysis of the electroencephalogram (EEG) signals was performed using Short-Time Fourier Transform (STFT). From the STFT coefficients, the spectrogram and instantaneous properties (frequency, bandwidth, power spectrum, band energy, local maxima, and band energy ratios) were investigated. From these properties, instantaneous features were generated by statistical analysis. Additive feature sets and reduced feature sets, formed by adding features successively and reducing features using the analysis of variance test respectively, were subjected to a tri-layered neural network classifier to evaluate the capability of the features to detect sleep arousal and normal sleep segments. 
Main results: The reduced feature set (Set 6) has proved to be efficacious in facilitating superior classification performance metrics (accuracy, sensitivity, specificity, and AUC of 89.14%, 83.52%, 89.49%, and 93.84% respectively). 
Significance: This efficient model can be incorporated with an automatic sleep apnea detection system where the estimation of hypopnea requires the detection of sleep arousal.
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State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset

Aiming to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine learning algorithm was trained and evaluated on 3423 polysomnograms of people with various sleep disorders. The model architecture is a U-net that accepts 50 Hz signals as input. We compared this algorithm with models trained on publicly available datasets, and evaluated these models using our clinical dataset, particularly with regard to the effects of different sleep disorders. In an effort to evaluate clinical relevance, we designed a metric based on the error of the predicted arousal index. Our models achieve an area under the precision recall curve (AUPRC) of up to 0.83 and F1 scores of up to 0.81. The model trained on our data showed no age or gender bias and no significant negative effect regarding sleep disorders on model performance compared to healthy sleep. In contrast, models trained on public datasets showed a small to moderate negative effect (calculated using Cohen's d) of sleep disorders on model performance. Therefore, we conclude that state-of-the-art arousal detection on our clinical data is possible with our model architecture. Thus, our results support the general recommendation to use a clinical dataset for training if the model is to be applied to clinical data.

Sleep arousal detection for monitoring of sleep disorders using one-dimensional convolutional neural network-based U-Net and bio-signals

PurposeSleep arousal detection is an important factor to monitor the sleep disorder.Design/methodology/approachThus, a unique nth layer one-dimensional (1D) convolutional neural network-based U-Net model for automatic sleep arousal identification has been proposed.FindingsThe proposed method has achieved area under the precision–recall curve performance score of 0.498 and area under the receiver operating characteristics performance score of 0.946.Originality/valueNo other researchers have suggested U-Net-based detection of sleep arousal.Research limitations/implicationsFrom the experimental results, it has been found that U-Net performs better accuracy as compared to the state-of-the-art methods.Practical implicationsSleep arousal detection is an important factor to monitor the sleep disorder. Objective of the work is to detect the sleep arousal using different physiological channels of human body.Social implicationsIt will help in improving mental health by monitoring a person's sleep.

Real-Time Detection of Sleep Arousals with a Head-Mounted Accelerometer.

Wearable electroencephalography (EEG) enables real-time interactions with the sleeping brain in real-life settings. An important parameter to monitor during these interactions are sleep arousals, i.e. temporary increases in EEG frequency, that compose sleep dynamics, but are challenging to detect without delay. We describe the development of an EEG- and accelerometer(ACC)-based sensing approach to detect arousals in real-time. We investigated the ability of these sensing modalities to timely and accurately detect arousals. When evaluated on 6 nights of mobile recordings, ACC had a median real-time delay of 2 s and was therefore better suited for an early detection of arousals than EEG (4.7 s). The detection performance was independent of sleep stages, but worked better on longer arousals. Our results demonstrate that a head-mounted ACC might be a cost-effective and easy-to-integrate solution for arousal detection where short delays are important or EEG signals are not available.

Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism

In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.

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

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.

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals.

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.

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

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.

Automatic Detection of Respiratory Effort Related Arousals With Deep Neural Networks From Polysomnographic Recordings.

Sleep disorders have become more common due to the modern lifestyle and stress. The most severe case of sleep disorders called apnea is characterized by a complete breaking block, leading to awakening and subsequent sleep disturbances. The automatic detection of sleep arousals is still challenging. In this paper, a novel method is presented to detect non-apnea sources of arousals during sleep using Polysomnography(PSG) recordings. After the preprocessing, a sequence-to-sequence deep neural network (DNNs) consisting of a series of Bidirectional long short-term memory (Bi-LSTM) layer, and fully connected layers were trained to classify samples in the segments. Initially, three different models were prepared for different datasets. Finally, obtaining the classification result through an ensemble model consisting of the three trained models. The result shows that the area under the receiver precision-recall curve (AUPRC) is 0.59 for the test dataset exceeding the performance of the classifiers in the existing literature.Clinical relevance- Analyzing Polysomnographic recordings is a time consuming a critical process yet to identify sleep disorders. These recordings span several hours and contain different data streams that include EEG, EMG, etc. This paper proposes a system that can automatically detect respiratory effort-related arousals using a deep neural network from Polysomnographic Recordings. By automating this process with a machine learning-based solution that can eliminate the manual process while facilitating further improvements of the system with future data.

Automatic Sleep Arousals Detection From Polysomnography Using Multi-Convolution Neural Network and Random Forest

Sleep arousals is a type of sleep disorder, which refers to the phenomenon of waking up and falling asleep again. Monitoring the number and duration of sleep arousals is a crucial aspect of sleep quality assessment. The detection of sleep arousals caused by apnea is relatively easy, and existing methods have been able to give high quality results. However, sleep arousals caused by non-apnea remains an ongoing challenge, and this is also the subject of PhysioNet Computing in Cardiology Challenge 2018. We proposed a non-apnea sleep arousals automatic detection algorithm based on polysomnography (PSG) data. We took 8 most representative signals selected from the 13 channels of PSG signals as input, conducted preliminary classification through multiple convolutional neural networks, and then sent the initial results to the random forest module for ensemble voting, and obtained the final judgment. We carried out some experiments using the CinC 2018 database. We grouped the original dataset reasonably, and based on each group of data, we trained a corresponding CNN, ensuring the balance of positive and negative samples during the training. Our 4-fold cross validation results for the AUROC and AUPRC were 0.953 and 0.552, which were better than the results of the team which ranked first in the CinC 2018.

Multimodal Sleep Signals-Based Automated Sleep Arousal Detection

Excessive sleep arousal affects one's sleep quality that would induce disease. Polysomnography is a powerful tool for sleep related monitoring. Clinically, there are being two kinds of causes on sleep arousal. One is apnea and hypopnea related arousal and the other is non-apnea and non-hypopnea arousal. The latter is relatively hidden and is difficult to determine in clinical. We aim to classify the sleep arousal caused by non-apnea and non-hypopnea from apnea and hypopnea related arousal. We propose an improved ensemble deep learning architecture that use a positional embedding based multi-head attention method to keep temporal relations of multimodal physiological signals. The experimental datasets are based on an open access dataset from the public cardiology challenge 2018. We conduct several groups of comparison experiments among our proposed convolutional-residual network with positional embedding and multi-head attention (CRPEMA) method and other methods that includes methods presented on the cardiology challenge 2018. The results show that CRPEMA has high efficiency and accuracy. When the parameters decrease by more than 50%, the accuracy is keeping improved. Experiment results reflect that CRPEMA outperforms others and obtains the Area Under the Precision-Recall curve (AUPRC) of 0.391.

Deepsleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

Background: Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with many negative effects including daytime sleepiness and sleep disorders. High-quality annotation of polysomnographic recordings is crucial for the diagnosis of sleep arousal disorders. Currently, sleep arousals are mainly annotated by human experts through looking at millions of data points manually, which requires considerable time and effort. Methods: We used the polysomnograms of 2,994 individuals from two independent datasets (i) PhysioNet Challenge dataset (n=994), and (ii) Sleep Heart Health Study dataset (n=2000) for model training (60%), validation (15%), and testing (25%). We developed a deep convolutional neural network approach, DeepSleep, to automatically segment sleep arousal events. Our method captured the long-range and short-range interactions among physiological signals at multiple time scales to empower the detection of sleep arousals. A novel augmentation strategy by randomly swapping similar physiological channels was further applied to improve the prediction accuracy. Findings: Compared with other computational methods in sleep study, DeepSleep features accurate (area under receiver operating characteristic curve of 0.93 and area under the precision recall curve of 0.55), high-resolution (5-millisecond resolution), and fast (10 seconds per sleep record) delineation of sleep arousals. This method ranked first in segmenting non-apenic arousals when evaluated on a large held-out dataset (n=989) in the 2018 PhysioNet Challenge. We found that DeepSleep provided more detailed delineations than humans, especially at the low-confident boundary regions between arousal and non-arousal events. This indicates that in silico annotations is a complement to human annotations and potentially advances the current binary label system and scoring criteria for sleep arousals. Interpretation: The proposed deep learning model achieved state-of-the-art performance in detection of sleep arousals. By introducing the probability of annotation confidence, this model would provide more accurate information for the diagnosis of sleep disorders and the evaluation of sleep quality. Funding Statement: This work is supported by NSF-US14-PAF07599 (CAREER: On-line Service for Predicting Protein Phosphorylation Dynamics Under Unseen Perturbations NSF), AWD007950 (Digital Biomarkers in Voices for Parkinson's Disease American Parkinson's Disease Association), University of Michigan O'Brien Kidney Translational Core Center, 19AMTG34850176 (American Heart Association and Amazon Web Services3.0 Data Grant Portfolio: Artificial Intelligence and Machine Learning Training Grants), and Michael J. Fox Foundation #17373. Declaration of Interests: YG receives personal payment from Eli Lilly and Company, Genentech Inc, F. Hoffmann-La Roche AG, and Cleerly Inc; holds equity shares at Cleerly Inc and Ann Arbor Algorithms Inc; receives research support from Merck KGaA as research contracts and Ryss Tech as unrestricted donation. Ethics Approval Statement: Not required.

Hybrid scattering-LSTM networks for automated detection of sleep arousals

Objective: Early detection of sleep arousal in polysomnographic (PSG) signals is crucial for monitoring or diagnosing sleep disorders and reducing the risk of further complications, including heart disease and blood pressure fluctuations. Approach: In this paper, we present a new automatic detector of non-apnea arousal regions in multichannel PSG recordings. This detector cascades four different modules: a second-order scattering transform (ST) with Morlet wavelets; depthwise-separable convolutional layers; bidirectional long short-term memory (BiLSTM) layers; and dense layers. While the first two are shared across all channels, the latter two operate in a multichannel formulation. Following a deep learning paradigm, the whole architecture is trained in an end-to-end fashion in order to optimize two objectives: the detection of arousal onset and offset, and the classification of the type of arousal. Main results and Significance: The novelty of the approach is three-fold: it is the first use of a hybrid ST-BiLSTM network with biomedical signals; it captures frequency information lower (0.1 Hz) than the detection sampling rate (0.5 Hz); and it requires no explicit mechanism to overcome class imbalance in the data. In the follow-up phase of the 2018 PhysioNet/CinC Challenge the proposed architecture achieved a state-of-the-art area under the precision-recall curve (AUPRC) of 0.50 on the hidden test data, tied for the second-highest official result overall.

Data mining techniques for detection of sleep arousals

Arousals are considered one of the main causes of daytime sleepiness. They impede the proper flow of sleep cycles and cause weariness. Manual scoring of arousals is time-consuming, requires expert knowledge, and has high inter-scorer variability. A major difficulty in detecting arousals automatically is the existing variance across patients. Based on data mining techniques, we present a different approach to the automatic detection of arousals that overcomes the hurdle of differences in signal characteristics across patients. Offline we used a training-set of adult patients to define a set of general rules to detect arousals (termed meta-rules). This was done by analyzing the correlations between occurrences of arousals and the EEG, EMG, pulse and SaO2 signals as follows: (1) each signal was mathematically projected into several spaces (termed projected-signals); (2) from each such projected-signal, the algorithm extracted time points that indicated meaningful changes (termed critical-points); (3) data mining techniques were applied to all the critical-points to discover patterns of repeating behavior; (4) classes of patterns which were highly correlated with manually scored arousals were formalized as meta-rules. Online we used a test-set of adult patients from two other different sleep laboratories. Using the meta-rules, the algorithm extracted individual rules for each patient (termed actual-rules), and used them to automatically detect the patients’ arousals. These arousals were significantly correlated ( R = 0.88, p < 0.0001; sensitivity = 75.2%, positive predictive value = 76.5%) with those detected manually by experts. Since the total number of arousals is a measure of sleep quality, this algorithm constitutes a novel approach to automatically estimate sleep quality.