Automatic neonatal sleep stage classification:A comparative study

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study

Automatic neonatal sleep stage classification: A comparative study

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study

EEG-Based Estimation of Human Reaction Time Corresponding to Change of Visual Event.

abstract: The human brain controls a person's actions and reactions. In this study, the main objective is to quantify reaction time towards a change of visual event and figuring out the inherent relationship between response time and corresponding brain activities. Furthermore, which parts of the human brain are responsible for the reaction time is also of interest. As electroencephalogram (EEG) signals are proportional to the change of brain functionalities with time, EEG signals from different locations of the brain are used as indicators of brain activities. As the different channels are from different parts of our brain, identifying most relevant channels can provide the idea of responsible brain locations. In this study, response time is estimated using EEG signal features from time, frequency and time-frequency domain. Regression-based estimation using the full data-set results in RMSE (Root Mean Square Error) of 99.5 milliseconds and a correlation value of 0.57. However, the addition of non-EEG features with the existing features gives RMSE of 101.7 ms and a correlation value of 0.58. Using the same analysis with a custom data-set provides RMSE of 135.7 milliseconds and a correlation value of 0.69. Classification-based estimation provides 79% & 72% of accuracy for binary and 3-class classication respectively. Classification of extremes (high-low) results in 95% of accuracy. Combining recursive feature elimination, tree-based feature importance, and mutual feature information method, important channels, and features are isolated based on the best result. As human response time is not solely dependent on brain activities, it requires additional information about the subject to improve the reaction time estimation.Dissertation/ThesisMasters Thesis Electrical Engineering 201

A convolutional neural network-based decision support system for neonatal quiet sleep detection

Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No. (2020) 22). To train and test the CNN, we extracted 12 prominent time and frequency domain features from 9 bipolar EEG channels. The CNN architecture comprised two convolutional layers with pooling and rectified linear unit (ReLU) activation. Additionally, a smoothing filter was applied to hold the sleep stage for 3 minutes. Through performance testing, our proposed method achieved impressive results, with 94.07% accuracy, 89.70% sensitivity, 94.40% specificity, 79.82% F1-score and a 0.74 kappa coefficient when compared to human expert annotations. A notable advantage of our approach is its computational efficiency, with the entire training and testing process requiring only 7.97 seconds. The proposed algorithm has been validated using leave one subject out (LOSO) validation, which demonstrates its consistent performance across a diverse range of neonates. Our findings highlight the potential of our algorithm for real-time neonatal sleep stage classification, offering a fast and cost-effective solution. This research opens avenues for further investigations in early-stage development monitoring and the assessment of neonatal health

A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates

Objective Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels, have long been recognized. Manual staging is resource intensive and time consuming, and considerable effort must be spent to ensure inter-rater reliability. There is thus great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC). Methods In this paper, we present a single-EEG-sensor ASSC technique based on the dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5 s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database with repeat recordings from 20 healthy young adults. Results We achieved very high classification sensitivity, specificity and accuracy of 96.2 ± 2.2%, 94.2 ± 2.3%, and 94.4 ± 2.2% across 20 folds, respectively, and a high mean F1 score (92%, range 90–94%) when a multi-class Naive Bayes classifier was applied. Conclusions Our method outperformed the accuracy of previous studies not only on different datasets but also on the same database

Towards Automating Sleep Stage Scoring to Diagnose Sleep Disorders

Overnight polysomnography (PSG) is an important tool used to characterize sleep and the gold standard procedure for diagnosing many sleep disorders. PSG is a non-invasive procedure that collects various physiological data, such as EEG, EMG, EOG and ECG signals. The data is then scored in a subjective, laborious and time-consuming process by sleep specialists who assign a sleep stage to every 30-second window of the data according to predefined scoring rules by the American Academy of Sleep Medicine (AASM). Finally, clinicians make a diagnosis based on this annotated data. Consequently, the current process is heavily dependent upon human factors, which can result in poor agreement between expert scorers, but inter-scorer reliability has been found to be only around 82%. In this study we developed an automatic sleep stage scoring method, using a likelihood ratio decision tree classifier, with the goal of improving the speed, reliability, accuracy and cost efficiency of the current PSG scoring process. The algorithm was developed using the AASM Manual for Scoring Sleep. We extracted features from various physiological recordings of the PSG, based on the predefined rules of the AASM Manual. The features were computed for each 30-second epoch, in either the time or the frequency domain. The most useful features were selected by looking at probability distributions for each metric conditioned on the sleep stage, and identifying the features giving the greatest separation between stages. Examples of meaningful features include the power in different frequency bands of EEG signals, EMG energy per epoch, and number of spindles per epoch, to mention a few. These features were then used as inputs to the classifier which assigned each epoch one of five possible stages:; N3, N2, N1, REM or Wake. The automatic scoring was trained and tested on PSG data from 39 healthy individuals (age range: 24.2±3.1 years) with no sleep disturbances. The overall scoring accuracy was 76.97% on the test set. Some of the stages, such as stage N2, have more distinctive characteristics and thus yielded a higher per-stage scoring accuracy, whereas the other stages, for example stages N1 and REM, got confused more easily, resulting in lower per-stage accuracies. As expected, most misclassifications occurred between adjacent sleep stages. Although this accuracy may at first seem low, it is likely that the stages that the tool classified inaccurately may be sleep stages that contribute to inter-scorer reliability. Therefore, we see this tool as assisting sleep scorers to enhance efficiency with the further goal of eventually improving inter-scorer reliability. Sleep stage scoring provides an important basis for diagnosis of sleep disorders in general. However, the detection of sleep disturbances is very costly and time-consuming, and relies on subjective measures. Automating the scoring process improves the efficiency and consistency of scoring procedures and offers a way to diagnose sleeping disorders in a more robust, quantitative manner

Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

Automated sleep stage detection and classification of sleep disorders

Studies have demonstrated that more than 1 million Australians experience some sort of sleep-related disorder in their lifetime [12]. In order to improve the diagnostic and clinical treatment of sleep disorders, the first important step is to identify or automatically detect the sleep stages. The most common method, known as the visual sleep stage scoring, can be a tedious and time-consuming process. Because of that, there is a need to create or develop an improved automatic sleep stage detection method to assist the sleep physician to efficiently and accurately evaluate the sleep stages of patients or non-patients. This research project consisted of two parts. The first part focused on the automatic sleep stages detection based on two individual bio-signals, which made up an overnight polysomnography (PSG), such as the electroencephalogram (EEG), and electrooculogram (EOG). Several features were extracted from these two bio-signals in the time and frequency domains. The decision tree and classification methods were utilised for the classification of the sleep stages. The second part of this project focused on the automatic classification of different sleep and psychiatric disorders, such as patients with periodic limb movements of sleep (PLMs), sleep apnea-hypopnea syndrome (SAHS), primary insomnia, schizophrenia and healthy sleep. Different PSG parameters were computed for the classification of sleep disorders, such as descriptive statistics of sleep architecture. In conclusion, the advantage of an automatic sleep stage detection method based on a single-channel EEG or EOG signal can be undertaken with portable sleep stage recording instead of full the PSG system, which includes multichannel bio-signals. An automatic classification method of sleep and psychiatric disorders based on the descriptive statistics of sleep architecture statistics was found to be an effective technique for screening sleep and psychiatric disorders. This classification method can assist physicians to quickly undertake a diagnostic procedure

Automated techniques for bat echolocation call analysis

Acoustic bat detectors are an extraordinarily valuable tool in bat research as they enable researchers to listen in on the otherwise secretive world of bats, providing the means to nqn-invasively survey and monitor bats in their natural habitats. Technological advances facilitate unprecedented data collection, considerably expanding the scope of field studies. However, the burden of manual analysis, and difficulty in identifying some species reliably from their calls, hampers the development of systematic survey and long- term monitoring methods. We developed a series of algorithms for the automated analysis of bat detector recordings, used to detect and extract calls from continuous recordings, and measure temporal and spectral call variables. By hand-labelling the .location of calls in field recordings, we were able to evaluate the accuracy of the automated method at detecting calls. Comparison on the same dataset with two conventional bioacoustic signal detectors revealed our algorithm was more accurate and robust. Using machine learning (ML) classification algorithms that learn to identify calls following training using a reference library, we developed a fully automated species identification system. Evaluation of the system was carried out by cross-validation of our reference call library, containing recordings of >5000 calls from known British species, comparing classifier predictions to ground- truth labels. The ML approach outperformed conventional statistical analysis using discriminant function analysis (DFA). We applied our novel system to two field studies that highlight its utility. Firstly, monitoring multi- species bat activity at a remote cave system over a period of three months, analysing >20,000 audio files to investigate temporal patterns in activity. Secondly, separating acoustically cryptic Myotis species from data collected in the Lake District National Park, to generate presence data for species distribution modelling, facilitatinq the creation of species-specific habitat suitability maps projected over the entire Park (ea, 3,300 km")

Physiopathologie du somnambulisme : étude de l’activité cérébrale en sommeil lent profond via la Tomographie d’Émission Monophotonique (TEMP) et l'analyse de connectivité fonctionnelle cérébrale

Le somnambulisme se caractérise par des comportements moteurs complexes au cours du sommeil, dans un état où persiste une altération des fonctions cognitives, du jugement et de la conscience. Bien que cette parasomnie affecte jusqu’à 4% des adultes, sa physiopathologie demeure peu documentée à ce jour. Conceptualisé au départ comme un trouble reflétant une transition incomplète du sommeil vers l’éveil, le somnambulisme est maintenant aussi considéré comme un trouble reflétant des anomalies au niveau du sommeil lent profond (SLP). L’objectif de la thèse est de documenter la physiopathologie du trouble à la lumière de ces conceptualisations, en caractérisant l’activité cérébrale de somnambules à l’éveil et en sommeil lent profond à l’aide de deux techniques novatrices : les analyses de connectivité fonctionnelle cérébrale et la tomographie par émission monophotonique. Ces deux techniques sont particulièrement indiquées pour l’étude du sommeil et, bien que largement utilisées pour décrire le sommeil régulier, celles-ci n’avaient jamais encore été utilisées pour décrire le SLP de somnambules. Dans une première étude, des analyses de connectivité fonctionnelle cérébrale ont permis d’investiguer les changements d’interdépendance et de synchronisation des signaux EEG de 27 somnambules. La période de 20 secondes immédiatement avant le déclenchement d’un épisode de somnambulisme a été comparée à la période survenant 2 minutes avant leur déclenchement. Les résultats montrent que les épisodes sont précédés par des changements dans la connectivité fonctionnelle cérébrale qui suggèrent le passage vers un état plus près de l’éveil: une diminution de la connectivité locale dans la bande delta, caractéristique du sommeil, ainsi qu’une augmentation de la connectivité dans la bande beta, caractéristique de l’éveil, sur de longs réseaux inter-hémisphériques impliquant les régions frontales, pariétales et occipitales. Ces résultats soulignent que la coexistence entre le sommeil et l’éveil qui sous-tend les épisodes se manifeste également sous forme de changements au niveau des réseaux de connectivité cérébrale et que des marqueurs de cette coexistence s’installent avant même les manifestations comportementales des épisodes. Cette coexistence suggérant des anomalies du processus de transition vers l’éveil, elle appuie par ailleurs la classification du somnambulisme dans la catégorie des troubles de l’éveil. Dans une deuxième étude, la tomographie par émission monophotonique a été utilisée afin de caractériser le SLP et l’éveil, suivant 24 heures de privation de sommeil, de 10 somnambules et 10 participants contrôles. Les résultats révèlent que les somnambules, lorsque comparés aux participants contrôles, montrent une diminution de la perfusion en SLP dans plusieurs régions frontales et pariétales, régions qui ont préalablement été associées à la génération du SLP et à l’occurrence d’épisodes. De plus, les résultats en SLP montrent une diminution de la perfusion dans le cortex préfrontal dorsolatéral et l’insula, ce qui est congruent avec des manifestations cliniques des épisodes. À l’éveil, une diminution de la perfusion est observée chez les somnambules dans plusieurs régions frontales et pariétales, ce qui peut être mis en lien avec les dysfonctions cognitives et fonctionnelles diurnes observées chez cette population. En résumé, cette thèse suggère que le somnambulisme est associé à des anomalies fonctionnelles cérébrales qui s’étendent au-delà des épisodes eux-mêmes, affectant la période précédant leur déclenchement, de même que le SLP et l’éveil suivant privation de sommeil. Ainsi, elle souligne l’importance d’en arriver à une compréhension de la physiopathologie du somnambulisme qui prenne en considération la façon dont ce trouble se manifeste en dehors des épisodes comportementaux.Somnambulism is characterized by the occurrence of complex motor behaviours during NREM sleep in a state in which consciousness, judgement and cognitive functions are altered. Although the disorder affects up to 4% of adults, its pathophysiology remains poorly understood. Initially viewed as a disorder reflecting an incomplete transition from sleep to wakefulness, sleepwalking is now also conceptualized as reflecting key anomalies in slow-wave sleep. The main objective of this thesis was to characterize the cerebral activity of sleepwalkers during wakefulness and during slow-wave sleep in order to elucidate the nature of episode occurrence as well as the disorder’s pathophysiology. EEG functional connectivity analysis and single-photon emission computed tomography (SPECT) are two innovative methods that have been widely used to describe normal sleep. However, these methods had not yet been used to investigate sleepwalkers’ slow wave sleep. In study 1, EEG functional connectivity analyses were used to investigate changes in EEG signal synchronization and interdependency in a group of 27 sleepwalkers who experienced a somnambulistic episode during slow-wave sleep. The 20-sec segment of sleep EEG immediately preceding each patient’s episode was compared with the 20-sec segment occurring two minutes prior to episode onset. Results show that episode onset is preceded by changes in EEG functional connectivity, including decreased delta connectivity in parietal and occipital regions and increased beta connectivity in symmetric inter-hemispheric networks implicating frontal, parietal and occipital areas. These results indicate that somnambulistic episodes are preceded by brain processes characterized by the co-existence of arousal and deep sleep and provide new insights into sleepwalking’s pathophysiology while bolstering its conceptualization as a disorder of arousal. In study 2, SPECT was used to investigate recovery slow-wave sleep and wakefulness following sleep deprivation in 10 sleepwalkers and 10 matched controls. When compared to controls, sleepwalkers showed decreased rCBF in frontal and parietal areas, regions previously associated with slow-wave sleep generation and episode occurrence. Additionally, reduced rCBF was found in the dorsolateral prefrontal cortex and insula during recovery slow-wave sleep, which is consistent with several clinical features of somnambulistic episodes. Reduced rCBF found during sleepwalkers’ resting-state wakefulness in frontal and parietal regions may be related to daytime cognitive and functional anomalies previously described in this population. Taken as a whole, the results from this thesis suggest that sleepwalking is characterized by cerebral functional anomalies that extend well beyond the episodes themselves. In fact, not only are such anomalies observed immediately preceding episode onset, but also more generally during sleepwalkers’ recovery slow-wave sleep and resting-state wakefulness following sleep deprivation. These findings highlight the importance of conceptualizing sleepwalking’s pathophysiology in a way that adequately accounts for how the disorder manifests itself outside of actual behavioural episodes