An Experimental Study of Classification Algorithms Training Performance

This thesis evaluates the training performance of classifiers in terms of Root Mean Square Error (RMSE), Training Time and Complexity. The study was based on different data set that were obtained from UCI machine learning database and tested by the WEKA software machine learning tools. The aim of this study is to experiment several classifiers with different data sets to find out the best classifier for a certain data set like nominal, numerical and both, according to the objective of this research

Real Time Sleep Detection System Using New Statistical Features of the Single EEG Channel

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. Many of the prior and current related studies use multiple EEG channels, and are based on 30s or 20s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, the aim of this work is to present a novel and efficient real time technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. First, we run our algorithm off line using the PhysioNet Sleep European Data Format (EDF) Database to classify six sleep stages. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Second, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance. Finally, we propose an effective EEG classification technique for detecting sleep to only prove that our algorithm is simple and works fast in real time in an efficient way using Neurosky Mindwave headset that gathers the user’s brain waves

Multi-Class SVM Based on Sleep Stage Identification Using EEG Signal

Currently, sleep disorders are considered as one of the major human life issues. Human sleep is a regular state of rest for the body in which the eyes are not only usually closed, but also have several nervous centers being inactive; hence, rendering the person either partially or completely unconscious and making the brain a less complicated network. This paper introduces an efficient technique towards differentiating sleep stages to assist physicians in the diagnosis and treatment of related sleep disorders. The idea is based on easily implementable filters in any hardware device and feasible discriminating features of the Electroencephalogram (EEG) signal by employing the one-against-all method of the multiclass Support Vector machine (SVM) to recognize the sleep stages and identify if the acquired signal is corresponding to wake, stage1, stage2, stage3 or stage4.The experimental results on several subjects achieve 92% of classification accuracy of the proposed work. A comparison of our proposed technique with some recent available work in the literature also presents the high classification accuracy performance

EEG Signal Analysis for Effective Classification of Brain States

EEG (Electroencephalogram) is a non-stationary signal that has been well established to be used for studying various states of the brain, in general, and several disorders, in particular. This work presents efficient signal processing and classification of the EEG signal. The digital filters used during decomposition of the input EEG signal have transfer functions which are simple and easily realizable on digital signal processors (DSP) and embedded systems. The features selected in this study; energy, entropy and variance; are among the most efficient and informative to analyze the EEG signal strength and distribution for detecting brain disorders such as seizure. Training and testing of the extracted features are performed using linear kernel (Support Vector Machine) SVM and thresholding in DSP algorithms and hardware, respectively. The experimental results for the digital signal processing algorithms show a high classification accuracy of 95% in the occurrence of seizure in epileptic patients. The techniques in this work are also under investigation for classifying other brain states/disorders such as sleep stages, sleep apnea and multiple sclerosis

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.https://doi.org/10.3390/e1809027