Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

Automatic sleep stage monitoring is an essential tool for the diagnosis and treatment of sleep-related disorders effectively. Although extensive studies focusing on single source data or information, such as single channel electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), with machine learning (ML) and deep learning (DL) have been presented in this field of automatic sleep staging, there are very limited literature's that have investigated the impact of multisource information fusion on ML or DL based schemes for the sleep stage classification. In addition, exploiting recurrent neural network (RNN) to learn temporal information from the sequences of multi-source inputs can bring significant improvement in classifying the sleep stage. Therefore, we aim to develop an multi-source data fusion, more specifically two source's signal fusion, enabled end-to-end convolutional recurrent neural network (CRNN) in order to perform automatic sleep stage classification in this article. In the proposed scheme, no hand crafted features is used to train the model and hence the proposed model can be called an end-to-end approach. The proposed CRNN model can extract time-invariant features from raw data of two input sources and can fuse them to learn temporally correlated features. Sleep-EDF expanded benchmark dataset is used in this study and we have employed Fpz-Cz EEG channel signal and EOG signal as input with 3 variants of RNN layer in model architecture separately in order to evaluate and investigate the performance. The proposed CRNN model with standard RNN layers can classify the Sleep-EDF dataset with maximum average accuracy of 90.30% and maximum Cohen's kappa coefficient of 86.86 compared other two RNN variant's layer (e.g., gated recurrent unit and long short term memory) based models which is very promising in the studies on classification of sleep stages as well as can be regarded as an alternative to conventional sleep staging.

Poor quality and poor duration of sleep have been associated with cognitive decline, diseases, and disorders. Therefore, sleep studies are imperative to recapitulate phenotypes associated with poor sleep quality and uncover mechanisms contributing to psychopathology. Classification of sleep stages, vigilance state bout durations, and number of transitions amongst vigilance states serves as a proxy for evaluating sleep quality in preclinical studies. Currently, the gold standard for sleep staging is expert human inspection of polysomnography (PSG) obtained from preclinical rodent models and this approach is immensely time consuming. To accelerate the analysis, we developed a deep-learning-based software tool for automated sleep stage classification in rats. This study aimed to develop an automated method for classifying three sleep stages in rats (REM/paradoxical sleep, NREM/slow-wave sleep, and wakefulness) using a deep learning approach based on single-channel EEG data. Single-channel EEG data were acquired from 16 rats, each undergoing two 24 h recording sessions. The data were labeled by human experts in 10 s epochs corresponding to three stages: REM/paradoxical sleep, NREM/slow-wave sleep, and wakefulness. A deep neural network (DNN) model was designed and trained to classify these stages using the raw temporal data from the EEG. The DNN achieved strong performance in predicting the three sleep stages, with an average F1 score of 87.6% over a cross-validated test set. The algorithm was able to predict key parameters of sleep architecture, including total bout duration, average bout duration, and number of bouts, with significant accuracy. Our deep learning model effectively automates the classification of sleep stages using single-channel EEG data in rats, reducing the need for labor-intensive manual annotation. This tool enables high-throughput sleep studies and may accelerate research into sleep-related pathologies. Furthermore, we provide over 700 h of expert-scored sleep data, available for public use in future research studies.

This paper presents a CNN-GRU deep learning model for classifying sleep stages. The Conventional sleep stage scoring method is a visual classification process, based on a set of biomedical signals such as Electroencephalogram (EEG) and Electrooculogram (EOG), where high human intervention is required. In this study, we proposed a deep neural network involving convolutional neural networks and gated recurrent units, to automatically extract the most appropriate features and sequence trends of PSG signals, without utilizing hand crafted features for scoring sleep stages. The proposed model, which uses multiple PSG channels, was evaluated using two data sets collected from 184 patients and 70 healthy subjects. The proposed multi-channel model showed 91.9 % of overall accuracy, while recall, precision, and f1 measures were approximately 92 % for patients. For healthy subjects, the multi-channel model showed 89.3 % overall classification accuracy. Recall, precision, and f1 measures showed approximately 89 %. The main model was adapted to utilize with a single EEG channel configuration, which yields 4 single-channel models for each data set. Therefore, the proposed model is capable of performing sleep stage classification using a single EEG channel without altering the model architecture.

Sleep is a natural periodic state of rest for the body, in which the eyes usually close and consciousness is completely or partially lost. Consequently, there is a decrease in bodily movements and responsiveness to external stimuli. Slow wave sleep is of immense interest as it is the most restorative sleep stage during which the body recovers from weariness. During this sleep stage, electroencephalographic (EEG) and electro-oculographic (EOG) signals interfere with each other and they share a temporal similarity. In this investigation we used the EEG and EOG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by certified sleep specialists based on RK rules. In this pilot study, we performed spectral estimation of EEG signals by Autoregressive (AR) modeling, and then used Itakura Distance to measure the degree of similarity between EEG and EOG signals. We finally calculated the statistics of the results and displayed them in an easy to visualize fashion to observe tendencies for each sleep stage. We found that Itakura Distance is the smallest for sleep stages 3 and 4. We intend to deploy this feature as an important element in automatic classification of sleep stages.

Sleep plays a vital part in humans health. The correct classification of sleep stages provides clinical information that can be used to diagnose sleep disorders in patients. The gold standard for sleep evaluation is polysomnography. On the other hand, polysomnography has several drawbacks, such as expense, inconvenience, and a long wait time at sleep laboratories. Advancements in technology enable the adoption and use of single-channel EEG at in-home sleep monitoring; it provides better sleep-stage accuracy levels. Industry and academic researchers continuously improve automatic sleep-stage classification using machine and deep learning in multi-channel EEG and single-channel EEG. However, the performance evaluation of single-channel against multi-channel EEG was not established. The performance of sleep staging using XGBoost, LGBM and voting classifiers models was verified in multi-channel and single-channel EEG. A similar number of features and hyperparameters were employed for classification in multi-channel and single-channel EEG for sleep-stage classification. Each model used five healthy subjects data from the Sleep-EDFx database. K-fold validation was used for training and testing. XGBoost, LGBM, and voting classifiers classified the sleep stages and achieved an accuracy of 87.96%, 86.24%, 88.25% in multi-channel and 86.81%, 86.81%, 87.1% in single-channel, respectively. The sleep-stage classification results demonstrate that single-channel EEG gives almost similar accuracy to multi-channel EEG.

Accurate detection of sleep stages and sleep events is essential for the effective diagnosis and treatment of sleep disorders. However, current state-of-the-art methods often fall short in integrating these multiple tasks simultaneously. This study introduces a novel multi-task deep-learning approach for the joint detection of sleep events and hypnogram construction in a single pass. The proposed method adapts state-of-the-art single-shot object detection techniques to multi-channel time-series data, enabling simultaneous classification and detection of sleeping events. Experiments were conducted using diverse input channel combinations, including electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), oxygen saturation, airflow, and thoracic-abdominal movements, as well as multiple output configurations regarding sleep stages, EEG arousals, and respiratory events, both individually and combined. Results demonstrate improved accuracy in detecting respiratory events and sleep stages with additional channels. The multi-task approach enhanced overall performance, benefiting from shared knowledge across tasks.

In this study, using the polysomnography data set, the classification of sleep stages was realized automatically with supervised learning method. In this study, sleep stage classification was carried out in three stages. In the first stage, the biomedical signal was divided into its independent components by Independent Component Analysis method. In the second stage, feature extraction was performed by using Mel Kepstrum Coefficient method. In the third stage, artificial neural networks were trained by using the extracted features and the sleep phases were estimated by using software architecture called Long ShortTerm Memory. As a result of the classification process performed in this way, the accuracy rate of the ten fold cross validation obtained for the binary classification (asleep / wake) was found to be 97,87%. For the five fold classification problem, the accuracy rate of the subject dependent algorithm and polysomnography data obtained using a single EEG channel was found to be 93,36% as a result of the ten-fold cross validation process.

Sleep stage classification is essential for clinical disease diagnosis and sleep quality assessment. Most of the existing methods for sleep stage classification are based on single-channel or single-modal signal, and extract features using a single-branch, deep convolutional network, which not only hinders the capture of the diversity features related to sleep and increase the computational cost, but also has a certain impact on the accuracy of sleep stage classification. To solve this problem, this paper proposes an end-to-end multi-modal physiological time-frequency feature extraction network (MTFF-Net) for accurate sleep stage classification. First, multi-modal physiological signal containing electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are converted into two-dimensional time-frequency images containing time-frequency features by using short time Fourier transform (STFT). Then, the time-frequency feature extraction network combining multi-scale EEG compact convolution network (Ms-EEGNet) and bidirectional gated recurrent units (Bi-GRU) network is used to obtain multi-scale spectral features related to sleep feature waveforms and time series features related to sleep stage transition. According to the American Academy of Sleep Medicine (AASM) EEG sleep stage classification criterion, the model achieved 84.3% accuracy in the five-classification task on the third subgroup of the Institute of Systems and Robotics of the University of Coimbra Sleep Dataset (ISRUC-S3), with 83.1% macro F1 score value and 79.8% Cohen's Kappa coefficient. The experimental results show that the proposed model achieves higher classification accuracy and promotes the application of deep learning algorithms in assisting clinical decision-making.

Introduction à la neuropsychologie des épilepsies

Sleep stage classification in EEG signals using the clustering approach based probability distribution features coupled with classification algorithms

Sleep is an important aspect of human body which alternatively decides the physical and mental conditions. It has been seen that several sleep-related disorders are affected with irrespective of different age group subjects. The most important part of diagnosis any types of sleep disorders is the classification of sleep stages. But the manual inspection of sleep-related behavior is quite difficult and more time-consuming process. Sometimes it leads toward several other neurological disorders. Therefore in this research work, we propose an automated sleep staging approach for the classification of the sleep stages. Mainly the proposed experiments followed three basic steps such as preprocessing feature extraction, feature selection, and classification. The entire experiment is conducted upon the ISRUC-Sleep dataset. The proposed model reported an overall accuracy of 97.73% using machine learning classification model.KeywordsElectroencephalographyAutomatic sleep stage analysisTime–frequency featuresMachine learning

In recent years, more biomedical studies have begun to use multimodal data to improve model performance. Many studies have used ablation for explainability, which requires the modification of input data. This can create out-of-distribution samples and lead to incorrect explanations. To avoid this problem, we propose using a gradient-based feature attribution approach, called layer-wise relevance propagation (LRP), to explain the importance of modalities both locally and globally for the first time. We demonstrate the feasibility of the approach with sleep stage classification as our use-case and train a 1-D convolutional neural network with electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) data. We also analyze the relationship of our local explainability results with clinical and demographic variables to determine whether they affect our classifier. Across all samples, EEG is the most important modality, followed by EOG and EMG. For individual sleep stages, EEG and EOG have higher relevance for awake and non-rapid eye movement 1 (NREM1). EOG is most important for REM, and EEG is most relevant for NREM2-NREM3. Also, LRP gives consistent levels of importance to each modality for the correctly classified samples across folds but inconsistent levels of importance for incorrectly classified samples. Our statistical analyses suggest that medication has a significant effect upon patterns learned for EEG and EOG NREM2 and that subject sex and age significantly affects the EEG and EOG patterns learned, respectively. Our results demonstrate the viability of gradient-based approaches for explaining multimodal electrophysiology classifiers and suggest their generalizability for other multimodal classification domains.

Introduction Most of the current automatic polysomnography sleep staging methods use multi-signal data and require a sequence of preceding and following epochs to score the stage of a specific epoch, which may not be desirable for analysis in real-time and/or in free-living conditions. We developed a deep learning-based sleep staging algorithm, namely SleepInceptionNet, that is designed to score each epoch using only single-channel electroencephalogram (EEG) data within that specific epoch. Methods Polysomnography data of 883 participants (937,975 thirty-second epochs) in the Multi-Ethnic Study of Atherosclerosis (MESA) obtained from the National Sleep Research Resource (NSRR) were randomly divided into a separate training/validation set of 194 participants and a test set of 689 participants. Each 30-second raw central EEG channel signal was transformed to time-frequency domain images using continuous wavelet transform method. Sleep stage in each epoch was obtained using SleepInceptionNet, in which the InceptionV3 convolutional neural network structure was trained and tuned on the time-frequency images of the training set to classify each epoch into one of the five stages of Wake, N1, N2, N3, or rapid eye movement (REM) sleep. Results Compared to the ground truth manually scored polysomnography sleep stages, SleepInceptionNet achieved an overall kappa agreement of 0.690 and overall weighted accuracy of 0.897. The model showed accuracy (mean±SD across the test set participants) of 0.940±0.067 in detecting Wake, 0.883±0.047 in detecting N1, 0.845±0.055 in detecting N2, 0.939±0.038 in detecting N3, and 0.930±0.038 in detecting REM epochs, in reference to manually scored polysomnography. Conclusion SleepInceptionNet showed a high agreement with manually scored polysomnography in epoch-by-epoch classification of sleep stages. This study demonstrates the viability of real-time, accurate sleep staging using a single-channel EEG, which could have a variety of applications such as delivery of on-demand interventions during specific sleep stages in free-living conditions. Support (If Any) MESA Sleep Ancillary study was funded by NIH-NHLBI Association of Sleep Disorders with Cardiovascular Health Across Ethnic Groups (RO1 HL098433). MESA is supported by NHLBI (HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01- HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169) and NCATS (UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420). NSRR was supported by NHLBI (R24 HL114473, 75N92019R002).

Sleep stages classification by fusing the time-related synchronization analysis and brain activations

Automatic sleep staging with a single-channel EEG based on ensemble empirical mode decomposition