An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms (original) (raw)
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Automatic classification of Sleep Stages on a EEG signal by Artificial Neural Networks
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Visual analysis of the physiological signals recorded at sleep time constitutes a heavy task for the clinician. In fact data quantity to be analyzed, generally corresponding to eight hours of recordings studied per 30s epochs, as well as the complexity of this analysis require a significant time. The objective of our work is to propose a tool for automatic analysis and decision-making based on artificial neural networks (ANN). In this paper, we present an outline of this tool and we propose to compare human and ANN performances on a simple case of vigilance states labeling. The first difficulty consists of the choice of representation for the physiological signals and in particular the electroencephalogram (EEG) which is regarded as the principal indicator of sleep stages. Once the representation is adopted, the following step is the design of the optimal ANN by a training and validation process on data set of a healthy adult. The results obtained, on average 76% of agreement betwee...
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A Novel Approach for Computer Assisted Sleep Scoring Mechanism using ANN
2020
Sleep analysis and its categories in sleep scoring system is considered to be helpful in an area of sleep research and sleep medicine. The scheduled study employs novel approach for computer assisted automated sleep scoring system using physiological signals and Artificial neural network. The data collected were recorded for seven hour, 30 second epoch for each subject. The data procured from the physiological signal was controlled and prepared to expel degenerated signals in order to extract essential data or features used for the study. As, it is known human body distributes its own electrical signals which is needed to be eliminated and these are known as artifacts and they are needed to be filtered out. In this study, signal filtering is achieved by using Butterworth Low-Pass filter. The features extracted were trained and classified using an Artificial Neural Network classifier. Even though, it is a highly complicated concept, using same in biomedical field when engaged with el...
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A Novel Method for Automated Diagnosis of Epilepsy using Complex-Valued Classifiers
IEEE journal of biomedical and health informatics, 2015
The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out the study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation (DTCWT) at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements-maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks (CVANN). The evaluation of the proposed method is conducted using the k-fold cross-validation methodology, reporting on classification accuracy, sensitivity and specificity. The proposed method is tested using a benchmark EEG dataset and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epil...
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—During the past decades, a great body of research has been devoted to automatic sleep stage scoring using the electroencephalogram (EEG). However, the results are not yet satisfactory to be used as a standard procedure in clinical studies. In this study, using recent developments in robust EEG phase extraction, a novel set of EEG-based features containing the Shannon entropy of the instantaneous analytical form envelope and frequencies of the EEG are proposed for sleep stage scoring. The proposed feature set is used to construct a distributed decision-tree classifier, with binary K-nearest neighbor (KNN) classifiers at each decision node. The decision-tree structure is designed by brute-force-search over various combinations of the proposed feature set. The performance of the proposed approach is evaluated over two available sleep EEG datasets acquired using single-channel EEG. The first set contains 20 healthy young subjects containing equal number of male and female, and the second one has been acquired from 140 adult subjects from both genders, with sleep disorder. The performance of the proposed method is tested versus state-of-the-art classifiers. The results demonstrate that the proposed method, resulted in overall accuracies of 88.97% and 83.17% over the two datasets, respectively. Considering the high performance and simplicity of the proposed scheme, the method can be of interest for clinical sleep disorder studies.
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Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG
Journal of Medical Systems, 2010
Analysis and classification of sleep stages is essential in sleep research. In this particular study, an alternative system which estimates sleep stages of human being through a multi-layer neural network (NN) that simultaneously employs EEG, EMG and EOG. The data were recorded through polisomnography device for 7 h for each subject. These collective variant data were first grouped by an expert physician and the software of polisomnography, and then used for training and testing the proposed Artificial Neural Network (ANN). A good scoring was attained through the trained ANN, so it may be put into use in clinics where lacks of specialist physicians.
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