Inherent Fuzzy Entropy for the Improvement of EEG Complexity Evaluation

© 2017 IEEE. In recent years, the concept of entropy has been widely used to measure the dynamic complexity of signals. Since the state of complexity of human beings is significantly affected by their health state, developing accurate complexity evaluation algorithms is a crucial and urgent area of study. This paper proposes using inherent fuzzy entropy (Inherent FuzzyEn) and its multiscale version, which employs empirical mode decomposition and fuzzy membership function (exponential function) to address the dynamic complexity in electroencephalogram (EEG) data. In the literature, the reliability of entropy-based complexity evaluations has been limited by superimposed trends in signals and a lack of multiple time scales. Our proposed method represents the first attempt to use the Inherent FuzzyEn algorithm to increase the reliability of complexity evaluation in realistic EEG applications. We recorded the EEG signals of several subjects under resting condition, and the EEG complexity was evaluated using approximate entropy, sample entropy, FuzzyEn, and Inherent FuzzyEn, respectively. The results indicate that Inherent FuzzyEn is superior to other competing models regardless of the use of fuzzy or nonfuzzy structures, and has the most stable complexity and smallest root mean square deviation

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

Класифікація стадій сну з використанням безконтактного біосенсора на основі нейронної мережі CNN-LSTM

Магістерська дисертація за темою «Класифікація стадій сну з використанням безконтактного біосенсора на основі нейронної мережі CNN-LSTM» виконана студентом кафедри біомедичної кібернетики Івановою Яною Олегівною зі спеціальності 122 «Комп’ютерні науки» за освітньо-професійною програмою «Комп’ютерні технології в біології та медицині» та складається зі: вступу, 4 розділів («Огляд літературних джерел», «Методи автоматичної класифікації стадії сну», «Розробка алгоритмів класифікації стадій сну за допомогою нейронних мереж», «Огляд результатів тестування розроблених моделей»), розділу зі стартап проекту, висновків до кожного з цих розділів; загальних висновків; списку використаних джерел, який налічує 77 джерела та 2 додатки. Загальний обсяг роботи 107 сторінок. Актуальність теми. Одна безсонна ніч знижує стійкість імунітету до інфекційних захворювань і швидкість реакції на зовнішні імпульси. А постійний дефіцит і зниження якості сну підвищують ризик розвитку серцево-судинних і ендокринних захворювань. Полісомнографія є золотим стандартом моніторингу сну, але вимагає, щоб пацієнти спали в лікарняних умовах, з великою кількістю електродів, та знаходилися під контролем експертного клінічного персоналу. Всі ці фактори роблять даний метод не дуже зручним, дорогим та недієздатним для щоденного моніторингу. Крім того, так званий ефект першої ночі при ПСГ може зменшити тривалість та ефективність сну. Таким чином, існує потреба в інструментах для тривалого моніторингу сну як здорових людей, з метою профілактики та дотримання гігієни сну, так і людей з порушеннями сну, для діагностики, відстеження динаміки захворювання і корекції терапії. У зв'язку з цим, розробка засобів для тривалого автоматичного визначення структури сну є актуальним завданням. Мета дослідження. Розробка та реалізація нейронної мережі для класифікації стадій сну з використанням безконтактного біосенсора. Об’єкт дослідження. Класифікація стадій сну. Предмет дослідження. Використання нейронної мережі для класифікації стадій сну. Методи дослідження. Методи машинного навчання для вирішення задач класифікації.Master's dissertation "Sleep stages classification using non-contact biosensor based on CNN-LSTM neural network" is performed by a student Yana Ivanova of the Department of Biomedical Cybernetics in the specialty 122 "Computer Science" in the educational program "Information Technologies in Biology and Medicine" and consists of: introduction, 4 sections ("Review of literature sources", "Methods of automatic sleep stages classification", "Development of algorithms for the sleep stages classification using neural networks", "Test results of developed models"), a startup project, conclusions; a list of sources, which includes 76 sources. The total volume of the work is 107 pages. Theme urgency. One sleepless night reduces the resistance of the immune system to infectious diseases and the speed of reaction to external impulses. And the constant deficit and decline in sleep quality increase the risk of cardiovascular and endocrine diseases. Polysomnography is the gold standard for sleep monitoring but requires patients to sleep in a hospital, with a large number of electrodes, and to be supervised by expert clinical staff. All these factors make this method not very convenient, expensive, and incapable of daily monitoring. In addition, the so-called first night effect of UGS may reduce the duration and effectiveness of sleep. Thus, there is a need for tools for long-term sleep monitoring of both healthy people, in order to prevent and maintain sleep hygiene, and people with sleep disorders, to diagnose, monitor the dynamics of the disease, and correct therapy. In this regard, the development of tools for long-term automatic determination of the structure of sleep is an urgent task. The aim of the study. Development and implementation of a neural network for the classification of sleep stages using a contactless biosensor. The object of study. Classification of sleep stages. The subject of study. Using the neural network to classify the stages of sleep. Research methods. Methods of machine learning to solve classification problems

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

Feature Extraction and Selection in Automatic Sleep Stage Classification

Sleep stage classification is vital for diagnosing many sleep related disorders and Polysomnography (PSG) is an important tool in this regard. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. The automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. In this research work, we focused on feature extraction and selection steps. The main goal of this thesis was identifying a robust and reliable feature set that can lead to efficient classification of sleep stages. For achieving this goal, three types of contributions were introduced in feature selection, feature extraction and feature vector quality enhancement. Several feature ranking and rank aggregation methods were evaluated and compared for finding the best feature set. Evaluation results indicated that the decision on the precise feature selection method depends on the system design requirements such as low computational complexity, high stability or high classification accuracy. In addition to conventional feature ranking methods, in this thesis, novel methods such as Stacked Sparse AutoEncoder (SSAE) was used for dimensionality reduction. In feature extration area, new and effective features such as distancebased features were utilized for the first time in sleep stage classification. The results showed that these features contribute positively to the classification performance. For signal quality enhancement, a loss-less EEG artefact removal algorithm was proposed. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy