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

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model.Comment: Accepted for publication in 41st International Engineering in Medicine and Biology Conference (EMBC), July 23-27, 201

A contribution for the automatic sleep classification based on the Itakura-Saito spectral distance

Sleep staging is a crucial step before the scoring the sleep apnoea, in subjects that are tested for this condition. These patients undergo a whole night polysomnography recording that includes EEG, EOG, ECG, EMG and respiratory signals. Sleep staging refers to the quantification of its depth. Despite the commercial sleep software being able to stage the sleep, there is a general lack of confidence amongst health practitioners of these machine results. Generally the sleep scoring is done over the visual inspection of the overnight patient EEG recording, which takes the attention of an expert medical practitioner over a couple of hours. This contributes to a waiting list of two years for patients of the Portuguese Health Service. In this work we have used a spectral comparison method called Itakura distance to be able to make a distinction between sleepy and awake epochs in a night EEG recording, therefore automatically doing the staging. We have used the data from 20 patients of Hospital Pulido Valente, which had been previously visually expert scored. Our technique results were promising, in a way that Itakura distance can, by itself, distinguish with a good degree of certainty the N2, N3 and awake states. Pre-processing stages for artefact reduction and baseline removal using Wavelets were applied.publishersversionpublishe

A Brief Summary of EEG Artifact Handling

There are various obstacles in the way of use of EEG. Among these, the major obstacles are the artifacts. While some artifacts are avoidable, due to the nature of the EEG techniques there are inevitable artifacts as well. Artifacts can be categorized as internal/physiological or external/non-physiological. The most common internal artifacts are ocular or muscular origins. Internal artifacts are difficult to detect and remove, because they contain signal information as well. For both resting state EEG and ERP studies, artifact handling needs to be carefully carried out in order to retain the maximal signal. Therefore, an effective management of these inevitable artifacts is critical for the EEG based researches. Many researchers from various fields studied this challenging phenomenon and came up with some solutions. However, the developed methods are not well known by the real practitioners of EEG as a tool because of their limited knowledge about these engineering approaches. They still use the traditional visual inspection of the EEG. This work aims to inform the researchers working in the field of EEG about the artifacts and artifact management options available in order to increase the awareness of the available tools such as EEG preprocessing pipelines

Automatic Sleep EEG Pattern Detection

Analýza mozkové aktivity je jednou z klícových vyšetrovacích metod v moderní spánkové medicíne a výzkumu.nalysis of recorded brain activity is one of the main investigation methods in modern sleep medicine and research

Feature selection and artifact removal in sleep stage classification

The use of Electroencephalograms (EEG) are essential to the analysis of sleep disorders in patients. With the use of electroencephalograms, electro-oculograms (EOG), and electromyograms (EMG), doctors and EEG technician can make conclusions about the sleep patterns of patients. In particular, the classification of the sleep data into various stages, such as NREM I-IV, REM, Awake, is extremely important. The EEG signal itself is highly sensitive to physiological and non-physiological artifacts. Trained human experts can accommodate for these artifacts while they are analyzing the EEG signal. However, if some of these artifacts are removed prior to analysis, their job will be become easier. Furthermore, one of the biggest motivations, of our team's research is the construction of a portable device that can analyze the sleep data as they are being collected. For this task, the sleep data must be analyzed completely automatically in order to make the classifications. The research presented in this thesis concerns itself with the denoising and the feature selection aspects of the teams' goals. Since humans are able to process artifacts and ignore them prior to classification, an automated system should have the same capabilities or close to them. As such, the denoising step is performed to condition the data prior to any other stages of the sleep stage neoclassicisms. As mentioned before, the denoising step, by itself, is useful to human EEG technicians as well. The denoising step in this research mainly looks at EOG artifacts and artifacts isolated to a single EEG channel, such as electrode pop artifacts. The first two algorithms uses Wavelets exclusively (BWDA and WDA), while the third algorithm is a mixture of Wavelets and In- dependent Component Analysis (IDA). With the BWDA algorithm, determining consistent thresholds proved to be a difficult task. With the WDA algorithm, the performance was better, since the selection of the thresholds was more straight-forward and since there was more control over defining the duration of the artifacts. The IDA algorithm performed inferior to the WDA algorithm. This could have been due to the small number of measurement channels or the automated sub-classifier used to select the denoised EEG signal from the set of ICA demixed signals. The feature selection stage is extremely important as it selects the most pertinent features to make a particular classification. Without such a step, the classifier will have to process useless data, which might result in a poorer classification. Furthermore, unnecessary features will take up valuable computer cycles as well. In a portable device, due to battery consumption, wasting computer cycles is not an option. The research presented in this thesis shows the importance of a systematic feature selection step in EEG classification. The feature selection step produced excellent results with a maximum use of just 5 features. During automated classification, this is extremely important as the automated classifier will only have to calculate 5 features for each given epoch