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The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals., (© 2024. The Author(s).)

Sleep disorder diseases have one of the major health issues across the world. To handle this issue the primary step taken by most of the sleep experts is the sleep staging classification. The whole visual inspection process is carried out manually by the sleep experts, which can be a highly time-consumed task and creates a lot of annotation errors due to more human interventions. In this study, we introduce an efficient and robust approach to improve the sleep staging accuracy. In this paper, we proposed an automated deep nine-layer one-dimensional convolution neural network for multi-class sleep staging classification (9 L-1D-CNN-SSC) using polysomnography (PSG) signals. The proposed 9 L-1D-CNN-SSC model comprises eleven layers with learnable parameters: nine convolution layers and two fully connected layers. The main objective of designing such a model is to achieve higher classification accuracy for multiclass sleep stages classifications with reduced learnable parameters. The proposed network architecture is tested on two different subgroups recordings of ISRUC-Sleep datasets namely ISRUC-Sleep subgroup1 (ISR-SG-I), and ISRUC-Sleep subgroup3 (ISR-SG-III). The proposed model is compiled with eight different individual experiments based on a single-channel electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and combinations of EEG + EOG+ EMG signals. The proposed 9 L-1D-CNN-SSC model achieved the highest classification accuracy of 99.03%, 99.50%, and 99.03% for three to five sleep stages classification, respectively with single-channel of EEG signals, similarly, the model achieved 98.93% for two-state sleep stage classification with EMG signals using the ISR-SG-I dataset. The same model achieved the highest classification accuracy of 98.88%, 98.76%, and 98.67% for three-five sleep stages classification with a single-channel EMG signal, and 99.24% for two-state sleep classification with single-channel EOG using ISR-SG-III dataset. It has been observed that the obtained results from the proposed 9 L-1D-CNN-SSC model give the best classification accuracy performance on multiclass sleep stages classification incomparable to the existing literature works. The developed 9 L-1D-CNN-SSC deep learning architecture is ready for clinical usage with high PSG data. [ABSTRACT FROM AUTHOR]

Sleep is one of the elements most vital to human life. However, the modern lifestyle continues to push people to neglect this critical requirement. With a vast majority of people falling victim to various sleep disorders, it has become increasingly essential to have a robust system for diagnosing and treating such ailments. Sleep stage classification is one of the primary steps for identifying sleep-related anomalies. Sleep stages are classified according to the frequency and nature of signals received during a polysomnography test. Since the early days, this has been performed manually with the help of trained technicians. However, manual scoring is often prone to error and subjectivity and requires tremendous time and effort. It is, therefore, essential to automatize this process. Several challenges from the correct selection of features remain to be faced in the machine learning-based sleep stage classification system. As an alternative, Deep Learning, capable of automatic feature extraction, proves far more reliable for this task. This experimental study analyses both techniques to compare and decide on a better approach. Three popular Machine Learning classifiers, namely Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), and a neural network comprising CNN and LSTM, have been trained on a vast base of diverse data. The proposed model reported an accuracy of 87.4% with CNN + LSTM. [ABSTRACT FROM AUTHOR]

Sleep is important part for human health and quality of life in the daily routine basis. However, numerous individuals face sleep problems due to rapid changes occurred in both social and professional lifestyles. These problems can lead to several neurological and physical disorder diseases, and therefore, decrease their overall life quality. Machine learning methods for automated sleep stage classification (ASSC) are a fundamental approach to evaluate and treat this public health challenge. The main objective of this study is to propose a high-effective and high-accuracy based multiple sleep staging classification model based on single-channel electroencephalogram (EEG) signals using machine learning (ML) model. The proposed automated sleep staging system followed four basic stages: signal preprocessing, feature extraction and screening, classification algorithms, and performance evaluation. In this research work, a novel method is applied for signal preprocessing, feature screening and classification models. In signal preprocessing we obtain the wiener filter techniques for removing the different types of artifacts from input sleep recordings. In feature extraction, we obtain a total of 28 features based on both time and frequency domain features and non-linear features. The relevant features are screened through ReliefF weight feature selection algorithm, and eliminating the redundant features using Pearson correlation coefficients. The important contribution of this research work is establishes two layers an ensembling learning stacking model for classifying the multiple sleep stages. Three different subgroups of ISRUC-Sleep (SG-I/SG-II/SG-III) subjects sleep recordings having different health condition obtained for our proposed experimental work. Comparing with the recent contributions on sleep staging performances using single-channel EEG signals, it has found that our proposed ensemble learning stacking model was reported excellent in sleep staging classification accuracy performance for five sleep stages classification task (CT-5). The overall classification accuracy reported as 99.34%, 90.8%, and 98.50% for SG-I, SG-II, and SG-III dataset, respectively. [ABSTRACT FROM AUTHOR]

• An automated SleepEEG staging system using machine learning. • Both linear and non-linear features are extracted. • Subject Age is found to be good recognition feature. • A novel machine learning model developed based on Ensemble learning. • Ensemble learning model to classify the brain EEG signal into five classes. Sleep staging is an important part of diagnosing the different types of sleep-related disorders because any discrepancies in the sleep scoring process may cause serious health problems such as misinterpretations of sleep patterns, medication errors, and improper diagnosis. The best way of analyzing sleep staging is visual interpretations of the polysomnography (PSG) signals recordings from the patients, which is a quite tedious task, requires more domain experts, and time-consuming process. This proposed study aims to develop a new automated sleep staging system using the brain EEG signals. Based on a new automated sleep staging system based on an ensemble learning stacking model that integrates Random Forest (RF) and eXtreme Gradient Boosting (XGBoosting). Additionally, this proposed methodology considers the subjects' age, which helps analyze the S1 sleep stage properly. In this study, both linear (time and frequency) and non-linear features are extracted from the pre-processed signals. The most relevant features are selected using the ReliefF weight algorithm. Finally, the selected features are classified through the proposed two-layer stacking model. The proposed methodology performance is evaluated using the two most popular datasets, such as the Sleep-EDF dataset (S-EDF) and Sleep Expanded-EDF database (SE-EDF) under the Rechtschaffen & Kales (R&K) sleep scoring rules. The performance of the proposed method is also compared with the existing published sleep staging methods. The comparison results signify that the proposed sleep staging system has an excellent improvement in classification accuracy for the six-two sleep states classification. In the S-EDF dataset, the overall accuracy and Cohen's kappa coefficient score obtained by the proposed model is (91.10%, 0.87) and (90.68%, 0.86) with inclusion and exclusion of age feature using the Fpz-Cz channel, respectively. Similarly, the Pz-Oz channel's performance is (90.56%, 0.86) with age feature and (90.11%, 0.86) without age feature. The performed results with the SE-EDF dataset using Fpz-Cz channel is (81.32%, 0.77) and (81.06%, 0.76), using Pz-Oz channel with the inclusion and exclusion of the age feature, respectively. Similarly the model achieved an overall accuracy of 96.67% (CT-6), 96.60% (CT-5), 96.28% (CT-4),96.30% (CT-3) and 97.30% (CT-2) for with 16 selected features using S-EDF database. Similarly the model reported an overall accuracy of 85.85%, 84.98%, 85.51%, 85.37% and 87.40% for CT-6 to CT-2 with 18 selected features using SE-EDF database. [ABSTRACT FROM AUTHOR]